Class: Tools::SemanticSearchTool

Inherits:
ApplicationTool show all
Defined in:
app/mcp/tools/semantic_search_tool.rb

Overview

MCP Tool for performing semantic search across all embedded content.
Uses pgvector embeddings to find content matching natural language queries.

Examples:

Search for showcases about snow melting

{ query: "snow melting under pavers", types: ["showcases"], limit: 5 }

Search across all content

{ query: "how to install floor heating", limit: 10 }

Constant Summary collapse

SENSITIVE_SEARCH_TYPES =

Recognised sensitive search types.

%w[support_notes support_communications].freeze

Class Method Summary collapse

Class Method Details

.call(query:, types: nil, limit: 10, _server_context: nil) ⇒ Object 



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# File 'app/mcp/tools/semantic_search_tool.rb', line 54

def call(query:, types: nil, limit: 10, _server_context: nil)
  limit = limit.to_i.clamp(1, 50)

  normalized = normalize_types(types)
  needs_sensitive = Array(types).any? { |t| SENSITIVE_SEARCH_TYPES.include?(t.to_s.downcase) }

  search_limit = needs_sensitive ? limit * 3.clamp(1, 50) : limit

  results = SemanticSearchService.search(
    query,
    types: normalized,
    limit: search_limit,
    exclude_sensitive: !needs_sensitive
  )

  results = filter_support_case_embeddables(results) if needs_sensitive
  results = results.first(limit)

  records = results.pluck(:record)
  items = records.grep(Item)
  product_lines = records.grep(ProductLine)
  all_pls = (items.filter_map(&:primary_product_line) + product_lines).uniq(&:id)
  if all_pls.any?
    paths = ProductLine.canonical_paths_for(all_pls)
    all_pls.each { |pl| pl.instance_variable_set(:@canonical_path, paths[pl.id]) }
  end

  json_response(
    query: query,
    total_results: results.size,
    results: results.map { |r| format_result(r) }
  )
end