Vekraris — Cloud Vector Database on ICP

Overview

Vekraris is a next-generation cloud vector database built on the Internet Computer Protocol (ICP). Designed for high performance, scalability, and security, Vekraris lets you store, search, and manage embeddings and high-dimensional data efficiently for AI, search, and data analytics applications.

Endpoints & Example Usage

Performs a hybrid (vector + keyword) search on your dataset.

Request:

curl --location 'http://localhost:3000/hybridSearch' \
--header 'Content-Type: application/json' \
--data-raw '{
  "userKey": "1234567",
  "dbName": "OmegaData",
  "account": "test@example.com",
  "query": "multi-factor authentication",
  "limit": 1
}'

Response:

[
  {
    "title": "System Scalability Test Results",
    "content": "Scalability tests show system handles 10,000 concurrent users with 95% uptime. Latency spikes observed at peak load. Recommended adding two more nodes to cluster for improved resilience.",
    "createdAt": "1752489221237955988",
    "score": 0.08027743473736361
  }
]

2. Get Embeddings

Retrieves stored embeddings from your database.

Request:

curl --location 'http://localhost:3000/getEmbeddings' \
--header 'Content-Type: application/json' \
--data-raw '{
  "userKey": "1234567",
  "dbName": "OmegaData",
  "account": "test@example.com",
  "start": 0,
  "limit": 200
}'

Response: Returns a list of embedding objects associated with your data. (Response schema varies by implementation.)

3. Query Embeddings

Finds the most relevant embeddings for a text query.

Request:

curl --location 'http://localhost:3000/queryEmbeddings' \
--header 'Content-Type: application/json' \
--data-raw '{
  "userKey": "1234567",
  "dbName": "OmegaData",
  "account": "test@example.com",
  "query": "incremental backups"
}'

Response:

[
  {
    "title": "System Scalability Test Results",
    "content": "Scalability tests show system handles 10,000 concurrent users with 95% uptime. Latency spikes observed at peak load. Recommended adding two more nodes to cluster for improved resilience.",
    "createdAt": "1752489221237955988",
    "similarity": 0.22472182796853496
  }
]

Key Features

  • Cloud-Native: Built for scalability and high availability on ICP.
  • Hybrid Search: Combines keyword and vector similarity for more relevant results.
  • Fast Retrieval: Optimized for rapid search across large vector datasets.
  • Secure: User and database-level API key authentication.
  • Rich Metadata: Store and retrieve custom metadata with every vector.

Tips

  • Replace the endpoint URL with your live API address when deploying.
  • Always keep your userKey and account information confidential.
  • Fine-tune the limit parameter for your application's performance needs.
  • Use descriptive dbName values for organizing multiple vector databases.