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Supercharge AI and Data Retrieval with Our Vector Database Integrated with RAGs and LLMs
Features Highlights
Vector Embeddings Storage
Integration with Document Datastore
High-Performance Vector Indexing
Support for Multiple Embedding Models
Real-Time Vector Search
Details ↓
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Snippet code for VectorDB


Supercharge AI and Data Retrieval with Our Vector Database Integrated with RAGs and LLMs

The Vector DB provides AI-powered semantic search and insights by encoding objects in multi-dimensional vectors, revealing contextual similarities for precise recommendations and lightning fast discoveries


import createClient from '@singlebase/singlebase-js'

const API_KEY = "[[my-api-key]]"

const singlebase = createClient({ api_key: API_KEY })  

// Insert one document with vectors
const { data, error } = await singlebase
    title: 'Hello SinglebaseCloud',
    content: 'How are you doing?',
    vector: [0.1, 0.5, 0.4]

if (data) {
  console.log(`ID: ${data[0]._key} - Title: ${data[0].title}`)
} else {
  console.error('Unable to login. Try again later')

VectorDB Features

Vector Embeddings Storage
Store vector embeddings generated by models from OpenAI, VertexAI, Cohere, and Huggingface, providing a robust foundation for AI applications.
Integration with Document Datastore
Seamlessly integrate with the DocumentDB datastore to leverage its flexible and dynamic data storage capabilities.
High-Performance Vector Indexing
Utilize advanced indexing techniques to efficiently organize and retrieve vector embeddings, optimizing performance for AI-driven queries.
Support for Multiple Embedding Models
Enable the use of multiple embedding models from various providers, allowing for a diverse range of AI applications and use cases.
Real-Time Vector Search
Perform real-time searches and similarity comparisons on vector embeddings, enabling instantaneous retrieval of relevant data.
LLM Integration
Integrate with large language models to enhance the capabilities of AI applications, enabling sophisticated natural language understanding and generation.
RAG for AI Applications
Facilitate retrieval-augmented generation by combining vector search with generation models, enhancing the accuracy and relevance of AI-generated content.
Fine-Grained Access Control
Implement fine-grained access control to secure vector data, allowing precise permissions at the document, collection, or field level.
Metadata Management
Manage metadata associated with vector embeddings, providing additional context and information for more accurate searches and analyses.

Let's get started with Singlebase

Start building your next AI+ project with unlimited usage for Vector DB, Relational Document DB, Search, Auth, Storage.

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