{
  "context": "This criteria evaluates how effectively the engineer uses the python-Levenshtein package's ratio() function to implement a duplicate detection system. The focus is on proper usage of the similarity ratio calculation capabilities to solve the product matching problem.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Uses Levenshtein.ratio()",
      "description": "The calculate_similarity() function uses Levenshtein.ratio() to compute similarity between two strings",
      "max_score": 30
    },
    {
      "name": "Correct return type",
      "description": "The calculate_similarity() function returns a float value between 0.0 and 1.0 as produced by Levenshtein.ratio()",
      "max_score": 10
    },
    {
      "name": "Threshold comparison logic",
      "description": "The is_duplicate() function correctly compares the similarity score against the threshold parameter to determine duplicates",
      "max_score": 20
    },
    {
      "name": "Reuses similarity calculation",
      "description": "The is_duplicate() function calls calculate_similarity() or uses Levenshtein.ratio() to get the similarity score rather than reimplementing the logic",
      "max_score": 15
    },
    {
      "name": "Catalog iteration",
      "description": "The find_duplicates() function iterates through the catalog list and calculates similarity for each item against the target",
      "max_score": 15
    },
    {
      "name": "Filtering by threshold",
      "description": "The find_duplicates() function correctly filters catalog items to include only those with similarity scores at or above the threshold",
      "max_score": 10
    }
  ]
}