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Vectra — The Unified Vector Database Client for Ruby

#rails #ruby #ai

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'])
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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

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

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