# KnowledgeBaseFaq — OSS-level markdown FAQ knowledge entries for the self-contained
# chatbot. Account-scoped. Supports keyword retrieval via pg_trgm index on title and
# an optional pgvector embedding column (populated by a later embedding step).
#
# This is intentionally SEPARATE from Enterprise-only Captain tables
# (captain_assistant_responses / captain_assistants) so it works without EE.
class CreateKnowledgeBaseFaqs < ActiveRecord::Migration[7.1]
  def change
    create_table :knowledge_base_faqs do |t|
      t.bigint :account_id, null: false
      t.string :title, null: false            # short topic/question label
      t.text :content, null: false            # markdown body / answer
      t.jsonb :topic_tags, default: [], null: false  # e.g. ["shipping", "refund"]
      t.string :source_filename               # original md filename (optional)
      t.vector :embedding, limit: 1536        # pgvector embedding (nullable; set later)
      t.timestamps
    end

    add_index :knowledge_base_faqs, :account_id
    add_index :knowledge_base_faqs, [:account_id, :title]
    # pg_trgm GIN index for fuzzy keyword search on title + content
    # (matches repo convention: gin + gin_trgm_ops, e.g. index_messages_on_content)
    add_index :knowledge_base_faqs, :title, using: :gin, opclass: :gin_trgm_ops, name: 'index_kbf_on_title_trgm'
    add_index :knowledge_base_faqs, :content, using: :gin, opclass: :gin_trgm_ops, name: 'index_kbf_on_content_trgm'
    # pgvector ivfflat index for embedding similarity search (only when embeddings exist)
    add_index :knowledge_base_faqs, :embedding, using: :ivfflat, opclass: :vector_cosine_ops, name: 'index_kbf_on_embedding'
  end
end