GraphCanon updated 1mo · GitHub synced 1mo
Decision brief
Embedbase is a TypeScript-based API designed to facilitate the creation of Large Language Model (LLM) powered applications via integrations with embeddings and vector databases.
Good fit when
- * Use Embedbase if you require direct integration capabilities specifically designed for embeddings and vector databases, like pgvector or Supabase.
- * Opt for Embedbase when your project benefits from a lightweight API that emphasizes simplicity in integrating machine learning functionalities without overwhelming setup.
Avoid when
- * Avoid using Embedbase if your application's technology stack cannot effectively integrate TypeScript, as its primary language support is in this framework and not others like Python.
- * Do not use it when you need extensive customization options for the vector database configurations beyond what pgvector or Supabase offers.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (601d since push)
- As of 1mo
- Provenance
- Not a fork · Organization account
- As of 1mo
- Security (OSV)
- No lockfile
- As of 1mo
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Install
npm install embedbaseHow it fits your stack(1)
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Evidence and technical details
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Overview
Embedbase provides a simple API for building applications powered by Large Language Models (LLMs) using embeddings and integration with vector databases.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Jul 22, 2026
- Languages
- typescript, python
Source: github.language+pyproject.toml · Jul 22, 2026
Categories
Tags
README
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 ...'
For agents
This page has a .md twin and JSON over the API.