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.
pip3 install --upgrade pyepsilla
npm install epsillajs
Then connect to the created database.
from pyepsilla import cloud
client = 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
const epsillajs = require('epsillajs');
const client = new epsillajs.EpsillaCloud({
projectID: 'PROJECT-ID', // Copied from the GUI code snippet
apiKey: 'YOUR-API-KEY' // Replace with your API Key
});
const db = new epsillajs.VectorDB(
'DB-ID', // Copied from the GUI code snippet
client
);
await db.connect();
