Epsilla Cloud
Get your cloud hosted Epsilla vector database up and running within 1 minute.
Last updated
Get your cloud hosted Epsilla vector database up and running within 1 minute.
Last updated
Create an Epsilla Cloud account at https://cloud.epsilla.com/, then sign in.
Navigate to the 'Configurations' tab within the project, create a new API Key, and keep it at a secure place.
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'.
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.
First, install Epsilla Python/JavaScript client.
pip3 install --upgrade pyepsilla
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
You can insert multiple records in a batch.
status_code, response = db.insert(table_name="MyTable",
records=[
{"ID": 1, "Doc": "Berlin", "Embedding": [0.05, 0.61, 0.76, 0.74]},
{"ID": 2, "Doc": "London", "Embedding": [0.19, 0.81, 0.75, 0.11]},
{"ID": 3, "Doc": "Moscow", "Embedding": [0.36, 0.55, 0.47, 0.94]},
{"ID": 4, "Doc": "San Francisco", "Embedding": [0.18, 0.01, 0.85, 0.80]},
{"ID": 5, "Doc": "Shanghai", "Embedding": [0.24, 0.18, 0.22, 0.44]}
]
)
status_code, response = db.query(
table_name="MyTable",
query_field="Embedding",
query_vector=[0.35, 0.55, 0.47, 0.94],
limit=2
)
Use the GUI to delete a table and delete a database.