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embedbasevector

Embedbase

All the tools you need to develop AI apps


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Check out the docs for more info.

What is it

Embedbase is a dead-simple API to help you use VectorDBs and LLMs without needing to host them!

Key features

  • Generate: use .generateText() to use 9+ LLMs
  • Semantic Search: use .add() to create a list of semantically searchable information and .search() to run semantic queries

Installation

npm i embedbase-js

import { createClient } from 'embedbase-js'
// initialize client
const embedbase = createClient(
  'https://api.embedbase.xyz',
  '<grab me here https://app.embedbase.xyz/>'
)
 
const question =
  'im looking for a nice pant that is comfortable and i can both use for work and for climbing'
 
// search for information in a pre-defined dataset and returns the most relevant data
const searchResults = await embedbase.dataset('product-ads').search(question)
 
// transform the results into a string so they can be easily used inside a prompt
const stringifiedSearchResults = searchResults
  .map(result => result.data)
  .join('')
 
const answer = await embedbase
  .useModel('openai/gpt-3.5-turbo')
  .generateText(`${stringifiedSearchResults} ${question}`)
 
console.log(answer) // 'I suggest considering harem pants for your needs. Harem pants are known for their ...'

Table of Contents

What are people building

The fastest way to get started with Embedbase is signing up for free to Embedbase Cloud.

Dashboard Screenshot

Docs and support

Check out our tutorials for step-by-step guides, how-to's, and best practices, our documentation is powered by GPT-4, so you can ask question directly.

Ask a question in our Discord community to get support.

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.