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https://github.com/different-ai/embedbase
A dead-simple API to build LLM-powered apps
https://github.com/different-ai/embedbase
ai artificial-intelligence chatgpt chatgpt-plugin embeddings gpt-4 machine-learning natural-language-processing openai pgvector supabase vector-database
Last synced: 6 days ago
JSON representation
A dead-simple API to build LLM-powered apps
- Host: GitHub
- URL: https://github.com/different-ai/embedbase
- Owner: different-ai
- License: mit
- Created: 2022-12-26T12:56:20.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-27T22:10:36.000Z (about 1 month ago)
- Last Synced: 2024-12-29T17:02:26.437Z (13 days ago)
- Topics: ai, artificial-intelligence, chatgpt, chatgpt-plugin, embeddings, gpt-4, machine-learning, natural-language-processing, openai, pgvector, supabase, vector-database
- Language: TypeScript
- Homepage: https://docs.embedbase.xyz
- Size: 23.4 MB
- Stars: 501
- Watchers: 9
- Forks: 53
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
Embedbase
Hosted embeddings-as-a-service
Check out the [docs](https://docs.embedbase.xyz) for more info.
## What is it
Embedbase is a dead-simple API to help you use [VectorDBs](https://learn.microsoft.com/en-us/semantic-kernel/concepts-ai/vectordb) 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`
```js
import { createClient } from 'embedbase-js'
// initialize client
const embedbase = createClient(
'https://api.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
- [Getting started](#getting-started)
- [Javascript SDK](#sdk)
- [Docs and support](#docs-and-support)
- [Integrations](#our-integrations)
- [Contributing](#contributing)
## What are people building
- [Recommendation Engines: AVA uses Embedbase to help their users find related notes](https://github.com/louis030195/obsidian-ava)
- [Chat with your data: Solpilot uses Embedbase to put smart contract integration on autopilot](https://app.solpilot.xyz/chat)
- [Talk to your docs: ChatGPT-powered search for markdown documentation](https://github.com/different-ai/chat-gpt-powered-nextra)
The fastest way to get started with Embedbase is signing up for free to [Embedbase Cloud](https://app.embedbase.xyz/).
## Docs and support
Check out our [tutorials](https://docs.embedbase.xyz) 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](https://discord.gg/pMNeuGrDky) to get support.
## Contributing
Please read [CONTRIBUTING.md](./CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.