rewrite generate_missing_mappings tool in the newly added Application…

…Command style

app/commands/generate_missing_mappings_command.rb

@@ -0,0 +1,318 @@
+# frozen_string_literal: true
+
+class GenerateMissingMappingsCommand < ApplicationCommand
+  MAX_RETRIES = 3
+  QDRANT_PORT = 6333
+  EMBEDDING_MODEL = "text-embedding-3-small"
+  MAPPING_GRADER_GPT_MODEL = "gpt-4"
+  LATEST_SHOPIFY_VERSION = "shopify/#{File.read("VERSION").strip}"
+
+  usage do
+    no_command
+  end
+
+  def execute
+    frame("Generating missing mappings") do
+      shopify_categories_missing_mapping_groups = search_unmapped_shopify_categories
+      return if shopify_categories_missing_mapping_groups.empty?
+
+      initialize_clients
+      generate_missing_mappings_for_groups(shopify_categories_missing_mapping_groups)
+    end
+  end
+
+  private
+
+  def search_unmapped_shopify_categories
+    shopify_categories_lack_mappings = nil
+    spinner("Searching shopify categories that lack mappings") do
+      shopify_categories_lack_mappings = []
+      all_shopify_category_ids = Set.new(Category.all.pluck(:id))
+      MappingRule.where(
+        input_version: LATEST_SHOPIFY_VERSION,
+      ).group_by(&:output_version).each do |output_version, mappings|
+        shopify_category_ids_from_mappings_input = Set.new(
+          mappings.map do |mapping|
+            mapping.input.product_category_id.split("/").last
+          end,
+        )
+        unmapped_category_ids = all_shopify_category_ids - shopify_category_ids_from_mappings_input
+        category_ids_full_names = unmapped_category_ids.sort.map do |id|
+          category_full_name = Category.find(id)&.full_name
+          [id, category_full_name] if category_full_name
+        end.compact.to_h
+        next if category_ids_full_names.empty?
+
+        shopify_categories_lack_mappings << {
+          input_taxonomy: mappings.first.input_version,
+          output_taxonomy: output_version,
+          category_ids_full_names: category_ids_full_names,
+        }
+      end
+    end
+    shopify_categories_lack_mappings
+  end
+
+  def initialize_clients
+    frame("Initializing clients for performing semantic search") do
+      @openai_client = create_openai_client
+      @qdrant_client = create_qdrant_client
+    end
+  end
+
+  def create_openai_client
+    OpenAI::Client.new(
+      access_token: "shopify-eyJtb2RlIjoidGVhbSIsInRlYW0iOjE1MjU3LCJwcm9qZWN0Ijo0MTQ5NywicmVwbyI6InByb2R1Y3QtdGF4b25vbXkiLCJlbnZpcm9ubWVudCI6ImRldiIsImVtYWlsIjoiaG9uZ21pbmcud2FuZ0BzaG9waWZ5LmNvbSJ9-v3kkQN43B9f1YVX1H+viPtRjGXg+rD1tfRNZLgUQeNo=",
+      uri_base: "https://openai-proxy.shopify.ai/v1",
+      request_timeout: 10,
+    )
+  end
+
+  def create_qdrant_client
+    ensure_qdrant_server_running
+    Qdrant::Client.new(url: "http://localhost:#{QDRANT_PORT}")
+  end
+
+  def ensure_qdrant_server_running
+    return if system("lsof -i:#{QDRANT_PORT}", out: "/dev/null")
+
+    command = "podman run -p #{QDRANT_PORT}:#{QDRANT_PORT} qdrant/qdrant"
+    pid = Process.spawn(command, out: "/dev/null", err: "/dev/null")
+    Process.detach(pid)
+    logger.info("Started Qdrant server in the background with PID #{pid}.")
+  end
+
+  def generate_missing_mappings_for_groups(missing_mapping_groups)
+    missing_mapping_groups.each do |missing_mapping_group|
+      input_taxonomy = missing_mapping_group[:input_taxonomy]
+      output_taxonomy = missing_mapping_group[:output_taxonomy]
+      index_name = output_taxonomy.gsub(%r{[/\-]}, "_")
+      frame("Generating mappings for #{input_taxonomy} -> #{output_taxonomy}") do
+        embedding_data = load_embedding_data(output_taxonomy)
+        index_embedding_data(embedding_data:, index_name:)
+        generate_and_evaluate_mappings_for_group(
+          missing_mapping_group:,
+          index_name:,
+        )
+      end
+    end
+  end
+
+  def load_embedding_data(output_taxonomy)
+    embeddings = nil
+    spinner("Loading embeddings for #{output_taxonomy}") do
+      files = Dir.glob(File.join("data/integrations/#{output_taxonomy}/embeddings", "_*.txt"))
+      embeddings = files.each_with_object({}) do |partition, embedding_data|
+        File.foreach(partition) do |line|
+          word, vector_str = line.chomp.split(":", 2)
+          vector = vector_str.split(", ").map { |num| BigDecimal(num).to_f }
+          embedding_data[word] = vector
+        end
+      end
+    end
+    embeddings
+  end
+
+  def load_destination_taxonomy_ids(output_taxonomy)
+    logger.debug("Loading destination taxonomy IDs for #{output_taxonomy}")
+    YAML.load_file("data/integrations/#{output_taxonomy}/full_names.yml").each_with_object({}) do |category, hash|
+      hash[category["full_name"]] = category["id"]
+    end
+  end
+
+  def index_embedding_data(embedding_data:, index_name:)
+    spinner("Indexing embeddings for #{index_name}") do
+      @qdrant_client.collections.delete(collection_name: index_name)
+      @qdrant_client.collections.create(
+        collection_name: index_name,
+        vectors: { size: 1536, distance: "Cosine" },
+      )
+
+      points = embedding_data.map.with_index do |(key, value), index|
+        {
+          id: index + 1,
+          vector: value,
+          payload: { index_name => key },
+        }
+      end
+
+      points.each_slice(100) do |batch|
+        @qdrant_client.points.upsert(
+          collection_name: index_name,
+          points: batch,
+        )
+      end
+    end
+  end
+
+  def generate_and_evaluate_mappings_for_group(
+    missing_mapping_group:,
+    index_name:
+  )
+    spinner("Generating and evaluating mappings for each category in the group") do
+      destination_taxonomy_ids_by_full_name = load_destination_taxonomy_ids(missing_mapping_group[:output_taxonomy])
+      mapping_file_path = "data/integrations/#{missing_mapping_group[:output_taxonomy]}/mappings/from_shopify.yml"
+      mapping_data = YAML.load_file(mapping_file_path)
+
+      disagree_messages = []
+      missing_mapping_group[:category_ids_full_names].each do |source_category_id, source_category_name|
+        generated_mapping = generate_mapping(
+          source_category_id:,
+          source_category_name:,
+          index_name:,
+          destination_taxonomy_ids_by_full_name:,
+        )
+        mapping_data["rules"] << generated_mapping[:new_entry]
+        disagree_messages << generated_mapping[:mapping_to_be_graded] if generated_mapping[:grading_result] == "No"
+      end
+
+      mapping_data["rules"].sort_by! { |rule| rule["input"]["product_category_id"] }
+      File.write(mapping_file_path, mapping_data.to_yaml)
+
+      write_disagree_messages(disagree_messages) if disagree_messages.any?
+    end
+  end
+
+  def generate_mapping(source_category_id:, source_category_name:, index_name:,
+    destination_taxonomy_ids_by_full_name:)
+    logger.debug("Generating mapping for #{source_category_name}")
+    category_embedding = get_embeddings(source_category_name)
+    top_candidate = search_top_candidate(query_embedding: category_embedding, index_name:)
+    destination_category_id = destination_taxonomy_ids_by_full_name[top_candidate]
+
+    new_entry = {
+      "input" => { "product_category_id" => source_category_id },
+      "output" => { "product_category_id" => [destination_category_id.to_s] },
+    }
+
+    mapping_to_be_graded = {
+      from_category_id: source_category_id,
+      from_category: source_category_name,
+      to_category_id: destination_category_id.to_s,
+      to_category: top_candidate,
+    }
+
+    logger.debug("Grading mapping for #{source_category_name} -> #{top_candidate}")
+    grading_result = grade_taxonomy_mapping(mapping_to_be_graded)
+
+    { new_entry: new_entry, mapping_to_be_graded: mapping_to_be_graded, grading_result: grading_result }
+  end
+
+  def get_embeddings(text)
+    with_retries do
+      response = @openai_client.embeddings(
+        parameters: {
+          model: EMBEDDING_MODEL,
+          input: text,
+        },
+      )
+      response.dig("data", 0, "embedding")
+    end
+  end
+
+  def search_top_candidate(query_embedding:, index_name:)
+    result = @qdrant_client.points.search(
+      collection_name: index_name,
+      vector: query_embedding,
+      with_payload: true,
+      limit: 1,
+    )
+    result["result"].first["payload"][index_name]
+  end
+
+  def grade_taxonomy_mapping(mapping)
+    with_retries do
+      response = @openai_client.chat(
+        parameters: {
+          model: MAPPING_GRADER_GPT_MODEL,
+          messages: [
+            { role: "system", content: system_prompts_of_taxonomy_mapping_grader },
+            { role: "user", content: [mapping].to_json },
+          ],
+          temperature: 0,
+        },
+      )
+      JSON.parse(response.dig("choices", 0, "message", "content")).first["agree_with_mapping"]
+    end
+  end
+
+  def with_retries
+    retries = 0
+    begin
+      yield
+    rescue StandardError => e
+      retries += 1
+      if retries <= MAX_RETRIES
+        logger.debug("Received error: #{e.message}. Retrying (#{retries}/#{MAX_RETRIES})...")
+        sleep(1)
+        retry
+      else
+        logger.fatal("Failed after #{MAX_RETRIES} retries.")
+        raise
+      end
+    end
+  end
+
+  def write_disagree_messages(disagree_messages)
+    File.open("tmp/mapping_update_message.txt", "a") do |file|
+      file.puts "❗AI Grader disagrees with the following mappings:"
+      disagree_messages.each do |mapping|
+        mapping.each { |key, value| file.puts "#{key}:#{value}" }
+        file.puts
+      end
+    end
+  end
+
+  def system_prompts_of_taxonomy_mapping_grader
+    <<~CONTEXT
+      You are a taxonomy mapping expert who evaluate the accuracy of product category mappings between two taxonomies.
+      Your task is to review and grade the accuracy of the mappings, Yes or No, based on the following criteria:
+      1. Mark a mapping as Yes, i.e. correct, if two categories of a mapping are highly relevant to each other and similar
+        in terms of product type, function, or purpose.
+        For example:
+          - "Apparel & Accessories" and "Clothing, Shoes & Jewelry"
+          - "Apparel & Accessories > Clothing > One-Pieces" and "Clothing, Shoes & Accessories > Women > Women's Clothing > Jumpsuits & Rompers"
+      2. Mark a mapping as No, i.e. incorrect, if two categories of a mapping are irrevant to each other
+        in terms of product type, function, or purpose.
+        For example:
+          - "Apparel & Accessories > Clothing > Dresses" and "Clothing, Shoes & Jewelry>Shoe, Jewelry & Watch Accessories"
+          - "Apparel & Accessories" and "Clothing, Shoes & Jewelry>Luggage & Travel Gear"
+      Note, the character ">" in a category name indicates the start of a new category level. For example:
+      "sporting goods > exercise & fitness > cardio equipment"'s ancestor categories are "sporting goods > exercise & fitness" and "sporting goods".
+      You will receive a list of mappings. Each mapping contains a from_category name and a to_category name.
+      e.g. user's prompt in json format:
+      [
+        {
+          "from_category_id": "111",
+          "from_category": "Apparel & Accessories > Jewelry > Smart Watches",
+          "to_category_id": "222",
+          "to_category": "Clothing, Shoes & Jewelry>Men's Fashion>Men's Watches>Men's Smartwatches",
+          },
+        {
+          "from_category_id": "333",
+          "from_category": "Apparel & Accessories > Clothing > One-Pieces",
+          "to_category_id": "444",
+          "to_category": "Clothing, Shoes & Accessories > Women > Women's Clothing > Outfits & Sets",
+          },
+      ]
+      You evaluate accuracy of every mapping and reply in the following format. Do not change the order of mappings in your reply.
+      e.g. your response in json format:
+      [
+        {
+          "from_category_id": "111",
+          "from_category": "Apparel & Accessories > Jewelry > Smart Watches",
+          "to_category_id": "222",
+          "to_category": "Clothing, Shoes & Jewelry>Men's Fashion>Men's Watches>Men's Smartwatches",
+          "agree_with_mapping": "Yes",
+        },
+        {
+        "from_category_id": "333",
+          "from_category": "Apparel & Accessories > Clothing > One-Pieces",
+          "to_category_id": "444",
+          "to_category": "Clothing, Shoes & Accessories > Women > Women's Clothing > Outfits & Sets",
+          "agree_with_mapping": "No",
+        },
+      ]
+    CONTEXT
+  end
+end