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An API to easily connect your data to ChatGPT

Project description


embedbasevector

Embedbase

An API to easily connect your data to ChatGPT


Discord PyPI

Open-source sdk & api to easily connect data to ChatGPT

Used by AVA and serving 100k request a day

Try the sandbox playground now · Request Feature · Report Bug

Check out the docs for more info.

Table of Contents

Examples

Please refer to examples in the documentation.

What are people building

Getting started

# start local postgres
docker-compose up
from 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.

Dashboard Screenshot

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 LLMs

Inserting 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.

Project details


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Source Distribution

embedbase-0.9.9.tar.gz (24.3 kB view hashes)

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