🚀 Vectra — Unified Vector Database Client for Ruby (Pinecone, Qdrant, Weaviate, pgvector, and more)

If you've ever built AI applications in Ruby — semantic search, RAG, recommendation engines, or embedding queries — you know how quickly it becomes tedious to support multiple vector databases. Each provider has its own API, client, and quirks.

Vectra solves this problem by providing a single, unified API for all popular vector DBs, freeing you from vendor lock-in. Write your code once and switch backends effortlessly — Pinecone, Qdrant, Weaviate, and even PostgreSQL with pgvector.


GitHub Repository

🧠 Why Vectra?

Vector databases are becoming a standard in modern AI applications, yet the Ruby ecosystem lacks a consistent way to work with them. Vectra changes that with:

✨ Provider-agnostic API

• One set of methods: upsert, query, delete, and more — no need to learn each vendor's SDK.
  Documentation
  

⚙️ Support for multiple databases

• Pinecone
  


• Qdrant
  


• Weaviate
  


• pgvector
  All through the same client.
  GitHub
  

💪 Production-ready

• Retry logic, configurable backoff, observability through metrics, and ready-to-use classes for production workloads.
  Docs
  

📦 Rails Integration

• Use has_vector DSL for ActiveRecord models — embedding fields can be automatically indexed and searched.
  GitHub
  

📚 Well-documented gem

• Guides, YARD documentation, and examples for each provider.
  Documentation
  

Example usage:

require 'vectra'



# Initialize client for any provider

client = Vectra::Client.new(

  provider: :pinecone,

  api_key: ENV['PINECONE_API_KEY'],

  environment: 'us-west-4'

)



# Upsert an embedding

client.upsert(

  vectors: [

    { id: 'doc-1', values: [0.1,0.2,0.3], metadata: { title: 'Hello' } }

  ]

)



# Query for similar vectors

results = client.query(vector: [0.1,0.2,0.3], top_k: 5)

results.each { |m| puts "#{m.id}: #{m.score}" }



# Delete

client.delete(ids: ['doc-1'])

The same API works without changes if you switch the client to Qdrant, Weaviate, or pgvector.

GitHub

💡 When Vectra is Useful

• Building applications with semantic search




• Implementing RAG (retrieval-augmented generation) with embeddings




• Avoiding vendor lock-in across vector databases




• Having a consistent Ruby API surface for all vector databases

📦 Where to Find It

• GitHub: [https://github.com/stokry/vectra




• Documentation: [https://vectra-docs.netlify.app/

If you’re working with embeddings and vector search in Ruby (especially in Rails apps), Vectra can save you hours of frustration and make your architecture future-proof — letting you switch backends without rewriting code