Code for "Adding Intelligent Search to a Rails application with Weaviate"

create_schema.rb

require "weaviate"

client = Weaviate::Client.new(
  url:     ENV['WEAVIATE_URL'],
  api_key: ENV['WEAVIATE_API_KEY'],

  # Configure Weaviate to use OpenAI to create vectors and use it for querying
  # You can also use Cohere or Hugging Face and pass their API key here instead
  model_service: :openai,
  model_service_api_key: ENV['OPENAI_API_KEY'] 
)

client.schema.create(
  class_name: "Recipes",                  # Name of the collection
  description: "A collection of recipes", # Description of the collection
  vectorizer: "text2vec-openai",          # OpenAI will be used to create vectors
  module_config: {               
    "qna-openai": {              # Weviate's OpenAI's Q&A module
      model: "text-davinci-003", # OpenAI's LLM to be used
      maxTokens: 3500,           # Maximum number of tokens to generate in the completion
      temperature: 0.0,          # How deterministic the output will be
      topP: 1,                   # Nucleus sampling
      frequencyPenalty: 0.0,
      presencePenalty: 0.0
    }
  },
  properties: [
    {
      dataType: ["int"],
      description: "Recipe ID",
      name: "recipe_id"          # Our PostgreSQL's recipes.id
    },
    {
      dataType: ["text"],
      description: "Recipe content",
      name: "content"            # Recipes' concatenated content
    }
  ]
)

query.rb

client.query.aggs(
  class_name: "Recipes",
  fields: "meta { count }"
)

client.query.get(
  class_name: "Recipes",
  limit: "1",
  fields: """
    recipe_id
    _additional {
      answer {
        result
      }
    }  
  """,
  ask: "{ question: \"What is a vegan dish that could be made for Thanksgiving?\" }"
)

client.query.get(
  class_name: "Recipes",
  limit: "1",
  fields: """
    recipe_id
    _additional {
      answer {
        result
      }
    }  
  """,
  ask: "{ question: \"What is a traditional French breakfast?\" }"
)

client.query.get(
  class_name: "Recipes",
  limit: "3",
  fields: "recipe_id content",
  near_text: "{ concepts: [\"latin american pescado\"] }"
)

recipe.rb

# app/models/recipe.rb

# Table name: recipes
#
#  id                :bigint
#  title             :string
#  description       :text
#  total_time        :integer
#  ingredients       :text
#  instructions      :text
#  nutrients         :jsonb
#  yields            :string

class Recipe < ActiveRecord::Base
  def text_blob
    [
      "Title: #{title}",
      "Description: #{description}",
      "Total time: #{total_time} minutes",
      "Ingredients: #{ingredients.join(", ")}",
      "Instructions: #{instructions}",
      "Nutrients: #{nutrients}",
      "Serving size: #{yields}"
    ]
  end
end

weaviate.rake

namespace :weaviate do
  require "weaviate"

  desc "Importing recipes into Weaviate"

  task import_recipes: :environment do
    Recipe.find_in_batches do |group|
      objects = group.map do |recipe|
        {
          class: "Recipes",            # Each object must contain the schema name
          properties: {
            content: recipe.text_blob, # See below for the formatted data
            recipe_id: recipe.id       # Recipe ID from the recipes PSQL table
          }
        }
      end

      weaviate_client.objects.batch_create(
        objects: objects
      )
    end
  end
end