Lance DB is an open source vector database that can be used for multimodal data. It can handle things like text and images. Lance has either a serverless flavor that you can sign up for, or you can run it on your own using a number of back end options to suit your needs.
In this blog post I am going to cover how you can get LanceDB set up and running on in your OCI account.
Lance has multiple options that you can use for your back end storage. For example you can configure Lance DB to use object storage, or you could configure it to use a filesystem. There is an article here that covers how to select a storage option and consider the tradeoffs. For example object storage is great for low cost, pretty much limitless storage without operational overhead, however object storage has the downside of being much lower performance and higher latency than a traditional filesystem.
Setting up an instance
The first thing I will do is spin up an OCI instance in my tenancy. I’m not going to cover this in the blog post as the process is fairly straight forward. In my case I am using Visual Studio Code’s remote connection feature to connect to my instance as that option gives me a good text editing experience, as well as a terminal connection.
Given that we are looking to interact with the vector database fairly heavily, I have created a decent sized instance. I spun up and instance with 32 OCPUs and 512 GB of RAM as I intend to use this instance for some testing. You should feel free to scale your instance down as appropriate for your workload.
Once I have the Oracle Linux I need to install a few items to be ready to move forward.
Setting up access to OCI Object Storage
The next thing you need to do is make sure that you can access OCI object storage via the AWS Command line. This is required because Lance DB will talk to OCI via the S3 compatibility layer. My friend Tony Markel did an excellent job of how to configure access to OCI Object storage via S3 connections here.
Follow Tony’s instructions first. When you run the command line:
[opc@lancedb ~]$ oci iam customer-secret-key create --display-name display-name --user-id ocid1.user.oc1..aaaaaaaafhyayxojro2llkejgvsejdr7j754zjeesekh2stai7oau3k6yura
{
"data": {
"display-name": "display-name",
"id": "08062f215XXXXXXXXXXXXXXX",
"inactive-status": null,
"key": "2yHCpMul0AnXXXXXXXXXXXXXXXXXX",
"lifecycle-state": "ACTIVE",
"time-created": "2025-08-14T19:31:36.588000+00:00",
"time-expires": null,
"user-id": "ocid1.user.oc1..aaaaaaaafhyayxojro2XXXXXXXXXXXXXXXXXXXXXXXXX"
},
The value for “id” becomes your AWS Access key, and the value for “key” becomes the value for your AWS Secret Access Key. You should then run the command
aws configure
When prompted, enter in the values from above as your Access key and secret key.
You should then be able to use the command:
aws --endpoint-url https://{Account Namespace}.compat.objectstorage.{Region}.oraclecloud.com s3 ls
To get a list of the Object storage buckets in your OCI account. Magic!
Connecting Lance DB to OCI Object Storage
Interacting with Lance DB is then done through Python, or any other supported technology. For my example I am loading a CSV file that has city data into a table. You can see the code below. This code clearly only works for datasets where we have enough memory to lead the table into memory. For larger data sets you will want to optimize the code better.
import lancedb
import pandas as pd
data = []
with open("/home/opc/LanceDB-Test/worldcities.csv", "r") as file:
next(file)
for line in file:
parts = line.strip().split(",")
if len(parts) >= 3:
city = parts[0].replace('"', '')
country = parts[4].replace('"', '')
population = parts[len(parts) - 2].replace('"', '')
latitude = parts[2].replace('"', '')
longitude = parts[3].replace('"', '')
if (population == "" or latitude == "" or longitude == ""):
print(f"Skipping entry due to missing data: {line.strip()}")
continue
try:
vector = [
len(city),
len(country),
float(population) if population else 0.0,
float(latitude) if latitude else 0.0,
float(longitude) if longitude else 0.0,
]
entry = {
"vector": vector,
"city": city,
"country": country,
"population": int(float(population)),
"latitude": float(latitude),
"longitude": float(longitude)
}
data.append(entry)
except ValueError:
print(f"Skipping entry due to conversion error: {line.strip()}")
db = lancedb.connect(
"s3://lancedb-bucket/",
storage_options={
"awsAccessKeyId": "XXXXXXXXXXXXXXXXXXXXXXXX",
"awsSecretAccessKey": "XXXXXXXXXXXXXXXXXXXXXXXX",
"endpoint": "https://{Namespace}.compat.objectstorage.{Region}.oraclecloud.com",
"region": "{Region}",
}
)
if "sample_table" in db.table_names():
db.drop_table("sample_table")
table = db.create_table("sample_table", data=data)
table.create_fts_index("city", replace=True)
Once you run your application, lance will create a structure that looks like the following in your object storage:
Querying sample data
Once your data has been loaded, you can run a Python job to query the data.
tbl = db.open_table("sample_table")
data = tbl.search().limit(100000).select(["city", "country", "population", "latitude", "longitude"]).to_list()
df = pd.DataFrame(data)
Keep in mind that object storage will provide you with an easy means for getting running. However if your application has high performance requirements, then you will want to leverage another storage option, such as Luster.
