{
  "context": "Evaluates how well the solution uses gensim's downloader hub to list available embeddings, load pre-trained vectors, and answer similarity/analogy queries while honoring cache controls. Only package-driven interactions with hub metadata, loading, and KeyedVectors querying are scored.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Hub metadata",
      "description": "`list_embeddings` pulls model names and vector sizes from `gensim.downloader.info()` (or `api.info`) rather than hard-coding, and returns entries sorted by name.",
      "max_score": 20
    },
    {
      "name": "Dimension filter",
      "description": "Applies `min_vector_size` using the vector size field from downloader metadata to include/exclude models like `glove-wiki-gigaword-50` vs `glove-wiki-gigaword-300`.",
      "max_score": 15
    },
    {
      "name": "Hub loading",
      "description": "Loads embeddings via `gensim.downloader.load(model_name, ...)` to obtain the keyed vectors object instead of custom downloads, and caches the loaded object for reuse within the process.",
      "max_score": 20
    },
    {
      "name": "Similar words",
      "description": "`most_similar` delegates to KeyedVectors similarity APIs (`most_similar`/`similar_by_word`) with the requested `topn`, returning words with descending scores for each seed.",
      "max_score": 20
    },
    {
      "name": "Analogy query",
      "description": "`solve_analogy` uses KeyedVectors analogy support (`most_similar` with positive/negative vectors) rather than manual vector math, and reuses the already loaded model.",
      "max_score": 15
    },
    {
      "name": "Cache control",
      "description": "Respects a provided `cache_dir` by pointing downloader calls to that location (e.g., `api.BASE_DIR` or `load(..., return_path=True)`), and subsequent calls avoid re-downloading when the same model name is requested.",
      "max_score": 10
    }
  ]
}