Check out the docs for more info.
Embedbase is a dead-simple API to help you use VectorDBs and Embeddings Models without needing to host them! You can use embedbase to customize LLM (like ChatGPT!) and automatically feed them the right information.
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-16k')
.generateText(`${stringifiedSearchResults} ${question}`)
console.log(answer) // 'I suggest considering harem pants for your needs. Harem pants are known for their ...'
- Recommendation Engines: AVA uses Embedbase to help their users find related notes
- Chat with your data: Solpilot uses Embedbase to put smart contract integration on autopilot
- Talk to your docs: ChatGPT-powered search for markdown documentation
The fastest way to get started with Embedbase is signing up for free to Embedbase Cloud.
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.
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.