Embedbase
An API to easily connect your data to ChatGPT
Open-source sdk & api to easily connect data to ChatGPT
Used by AVA and serving 100k request a day
Check out the docs for more info.
Table of Contents
Examples
Please refer to examples in the documentation.
What are people building
- AVA uses Embedbase to help their users find related notes
- Solpilot uses Embedbase to put smart contract integration on autopilot
- ChatGPT-powered search for markdown documentation
Getting started
# start local postgres
docker-compose upfrom embedbase import get_app
from embedbase.database.postgres_db import Postgres
from embedbase.embedding.openai import OpenAI
async def custom_middleware(request, call_next):
# customise as you prefer :)
start_time = time.time()
response = await call_next(request)
process_time = time.time() - start_time
response.headers["X-Process-Time"] = str(process_time)
return response
app = (
get_app()
.use_middleware(custom_middleware)
.use_embedder(OpenAI("<your key>"))
.use_db(Postgres())
).run()uvicorn main:app
🔥 Embedbase now runs! Time to ship your product
Managed Instance
The fastest way to get started with Embedbase is signing up for free to Embedbase Cloud.
How to use
SDK
npm i embedbase-js
import { createClient } from 'embedbase-js'
const question = 'What can I do with Embedbase API?'
const embedbase = createClient(
'https://api.embedbase.xyz',
'api-key')
const context = await embedbase
.dataset('embedbase-docs')
.createContext('What can I do with Embedbase API?', { limit: 3 });
console.log(context)
[
"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...",
]
// refer to https://github.com/openai/openai-node for the exact api
openai.createCompletion(
`Write a response to question: ${question}
based on the follwing context ${context.toString()}`
)
// answer:
// You can use the Embedbase API to store unstructured data and then use the data to connect it to LLMsInserting data
const URL = 'http://localhost:8000'
const VAULT_ID = 'people'
// if using the hosted version
const API_KEY = '<https://app.embedbase.xyz/signup>'
fetch(`${URL}/v1/${VAULT_ID}`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
// if using the hosted version, uncomment
// 'Authorization': `Bearer ${API_KEY}`
},
body: JSON.stringify({
documents: [{
data: 'Elon is sipping a tea on Mars',
}],
}),
});Searching
fetch(`${URL}/v1/${VAULT_ID}/search`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
// 'Authorization': `Bearer ${API_KEY}`
},
body: JSON.stringify({
query: 'Something about a red planet',
}),
});Result:
{
"query": "Something about a red planet",
"similarities": [
{
"score": 0.828773,
"id": "ABCU75FEBE",
"data": "Elon is sipping a tea on Mars",
}
]
}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.
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