Vectra

gem 'vectra-client'

require 'vectra'

# Initialize client (works with any provider)
client = Vectra::Client.new(
  provider: :pinecone,
  api_key: ENV['PINECONE_API_KEY'],
  environment: 'us-west-4'
)

# Upsert vectors
client.upsert(
  vectors: [
    { id: 'doc-1', values: [0.1, 0.2, 0.3], metadata: { title: 'Hello' } },
    { id: 'doc-2', values: [0.4, 0.5, 0.6], metadata: { title: 'World' } }
  ]
)

# Search
results = client.query(vector: [0.1, 0.2, 0.3], top_k: 5)
results.each { |match| puts "#{match.id}: #{match.score}" }

# Delete
client.delete(ids: ['doc-1', 'doc-2'])

# Pinecone
client = Vectra.pinecone(api_key: ENV['PINECONE_API_KEY'], environment: 'us-west-4')

# Qdrant (local)
client = Vectra.qdrant(host: 'http://localhost:6333')

# Qdrant (cloud)
client = Vectra.qdrant(host: 'https://your-cluster.qdrant.io', api_key: ENV['QDRANT_API_KEY'])

# Weaviate
client = Vectra.weaviate(host: 'http://localhost:8080', api_key: ENV['WEAVIATE_API_KEY'])

# pgvector (PostgreSQL)
client = Vectra.pgvector(connection_url: 'postgres://user:pass@localhost/mydb')

class Document < ApplicationRecord
  include Vectra::ActiveRecord

  has_vector :embedding,
    provider: :qdrant,
    index: 'documents',
    dimension: 1536
end

# Auto-indexes on save
doc = Document.create!(title: 'Hello', embedding: [0.1, 0.2, ...])

# Search
Document.vector_search(embedding: query_vector, limit: 10)