Deploying Embedbase

For any help or assistance with deployment, book a demo (opens in a new tab) and we will help you deploy your own instance.

First, install Embedbase Python package and Uvicorn (opens in a new tab) to run it:

pip install embedbase uvicorn

Using Postgres

Run Postgres in Docker:

docker run --name postgres -e POSTGRES_DB=embedbase -e POSTGRES_PASSWORD=localdb -p 5432:5432 -p 8080:8080 -d ankane/pgvector

Basic entrypoint that uses Postgres & OpenAI (get your key (opens in a new tab)):

main.py

import os
from embedbase import get_app
from embedbase.database.postgres_db import Postgres
from embedbase.embedding.openai import OpenAI
 
app = get_app().use_db(Postgres()).use_embedder(OpenAI("<your key>")).run()

Run:

uvicorn main:app

Using Supabase

Basic entrypoint that uses Supabase & OpenAI (get your key (opens in a new tab)):

main.py

import os
from embedbase import get_app
from embedbase.database.supabase_db import Supabase
from embedbase.embedding.openai import OpenAI
 
app = get_app().use_db(Supabase("<your url>", "<your key>")).use_embedder(OpenAI("<your key>")).run()

There are multiple ways to use Supabase, either running it locally (opens in a new tab) or using their hosted instance.

First, create a supabase project on https://supabase.com (opens in a new tab).

Setting up Supabase

First, follow this guide (opens in a new tab) to setup Supabase.

🎉 Now you should be able to run Embedbase with Supabase

uvicorn main:app

Additional information

If you are wondering where the 1536comes from, it is the dimension of the vector generated by OpenAI technology (embeddings) (opens in a new tab), if you use something else, such as cohere.ai it would be 4096. Currently we only support OpenAI to build embeddings but you can easily implement another system, check customisation (opens in a new tab).

Deploy on Render.com

You can find the instructions here (opens in a new tab).

Cloud Run deployment

Embedbase makes it easy for you to deploy your own instance. This assures you're in control of your data end to end.

Setup

# login to gcloud
gcloud auth login
 
PROJECT_ID=$(gcloud config get-value project)
 
# Enable container registry
gcloud services enable containerregistry.googleapis.com
 
# Enable Cloud Run
gcloud services enable run.googleapis.com
 
# Enable Secret Manager
gcloud services enable secretmanager.googleapis.com
 
# create a secret for the config
gcloud secrets create EMBEDBASE --replication-policy=automatic
 
# add a secret version based on your yaml config
gcloud secrets versions add EMBEDBASE --data-file=config.yaml
 
IMAGE_URL="gcr.io/${PROJECT_ID}/embedbase:0.0.1"
 
docker buildx build . --platform linux/amd64 -t ${IMAGE_URL} -f ./docker/Dockerfile
 
docker push ${IMAGE_URL}
 
gcloud run deploy embedbase \
  --image ${IMAGE_URL} \
  --region us-central1 \
  --allow-unauthenticated \
  --set-secrets /secrets/config.yaml=EMBEDBASE:1
 
# getting cloud run url
gcloud run services list --platform managed --region us-central1 --format="value(status.url)" --filter="metadata.name=embedbase"

If you don't want to use Google Cloud Run, consider:

Run embedbase locally in Python Customization