Create an Epsilla Cloud account at https://cloud.epsilla.com/, then sign in. You will get $25 free credit.
2. Get your project API Key
Navigate to the 'Configurations' tab within the project, create a new API Key, and keep it at a secure place.
3. Create a vector database, then create a table
Navigate to the 'Resource' tab, and click 'Create Vector Database'.
Give the database a name, and click 'Create'. It takes a few seconds to spin up a vector database.
Within the newly created database, create a new table. Give the table a name. Adjust the table schema according to your business logic, then click 'Create'.
4. Use GUI to CRUD data to the table.
Epsilla automatically generates sample queries for the table. Start with inserting some sample data:
Then query the table with top K semantic similarity search.
Switch between Shell, Python, and JavaScript tags, and click the 'Copy' button to copy the curl command, Python code snippet, and JavaScript code snippet of the query.
Copying the code snippet is the easiest way to integrate with your application as it already has the project_id and db_id prefilled.
Remember to replace "YOUR-API-KEY" part with the API Key you get earlier.
5. Connect to Epsilla
First, install Epsilla Python/JavaScript client.
pip3install--upgradepyepsilla
npminstallepsillajs
Then connect to the created database.
from pyepsilla import cloudclient = cloud.Client( project_id="PROJECT-ID", # Copied from the GUI code snippet api_key="YOUR-API-KEY"# Replace with your API Key)db = client.vectordb(db_id="DB-ID")# Copied from the GUI code snippet
constepsillajs=require('epsillajs');constclient=newepsillajs.EpsillaCloud({ projectID:'PROJECT-ID',// Copied from the GUI code snippet apiKey:'YOUR-API-KEY'// Replace with your API Key});constdb=newepsillajs.VectorDB('DB-ID',// Copied from the GUI code snippet client ); awaitdb.connect();
// searchconstquery=awaitdb.query('MyTable', { queryField:"Embedding",// query field queryVector: [0.35,0.55,0.47,0.94],// query vector limit:2// top K });