KBaaS Vector Search

1.0.1

Overview

Description

The vector search service is responsible for building and querying vector indices, as well as returning nearest-neighbor results for retrieval-augmented generation (RAG) workflows. Inputs: text to embed, index/manage requests, and query vectors; Outputs: scored nearest documents, ids, and vector metadata. Typical responsibilities: manage vector store (indexing, inserts, deletes), provide similarity search endpoints. This software stack is built on top of the Nvidia cuVS library.

Products using this container

Slangit KBaaS

Slangit's KBaaS is an AI-engine developed to enable organizations (Government and Enterprises) to retrieve information from their data using various forms of knowledge retrieval techniques.

Containerized application

Security

Technical information

General information

The following information was extracted from the containerfile and other sources.

Summary	Vector Search service for Slangit
Description	The Universal Base Image is designed and engineered to be the base layer for all of your containerized applications, middleware and utilities. This base image is freely redistributable, but Red Hat only supports Red Hat technologies through subscriptions for Red Hat products. This image is maintained by Red Hat and updated regularly.
Provider	Slangit Technologies FZO
Maintainer	Support <support@slangit.ai>

Technical information

The following information was extracted from the containerfile and other sources.

Repository name	vector-search
Image version	1.0.1
Architecture	amd64

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Registry

Repository

Manifest List Digest

Image Digest

This certified image is distributed by a third party registry. Use the information above to pull the image or view pulling certified images from third party registries for more information.

Getting the Vector Search Image

Prerequisites

OpenShift CLI (oc) installed
AWS credentials configured
Docker CLI installed

Method 1: Using OpenShift Internal Registry (Recommended)

Step 1: Login to OpenShift ROSA cluster

# Login to OpenShift ROSA cluster
oc login --server=https://api.slangit-rosa.ies1.p3.openshiftapps.com:443 --web

Step 2: Login to Internal OpenShift Registry

docker login default-route-openshift-image-registry.apps.rosa.slangit-rosa.ies1.p3.openshiftapps.com \
  -u $(oc whoami) -p $(oc whoami -t)

Step 3: Pull the Vector Search Image

docker pull default-route-openshift-image-registry.apps.rosa.slangit-rosa.ies1.p3.openshiftapps.com/ai-service/vector_search:1.0.1

Notes

Docker is only required if you want to run the image locally.
For deployment in OpenShift, the internal image can be referenced directly in your manifests.
For further support or questions, contact: support@slangit.ai

Description

Products using this container

Slangit KBaaS

Published

Release category

Size

Digest

General information

Technical information

Getting the Vector Search Image

Prerequisites

Method 1: Using OpenShift Internal Registry (Recommended)

Step 1: Login to OpenShift ROSA cluster

Step 2: Login to Internal OpenShift Registry

Step 3: Pull the Vector Search Image

Notes

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KBaaS Vector Search

Description

Products using this container

Slangit KBaaS

Published

Release category

Size

Digest

General information

Technical information

Getting the Vector Search Image

Prerequisites

Method 1: Using OpenShift Internal Registry (Recommended)

Step 1: Login to OpenShift ROSA cluster

Step 2: Login to Internal OpenShift Registry

Step 3: Pull the Vector Search Image

Notes