content: [project]
  name = "semantic-merkle-tree"
  version = "0.1.0"
  description = """
  A meaning-aware Merkle tree that understands semantic changes, not just byte-level differences.
  
  Semantic Merkle Tree (SMT) is a hierarchical data structure that combines traditional Merkle tree hashing with AI-powered semantic embeddings. Instead of treating every change as significant, SMT only propagates updates when the *meaning* of content changes beyond a configurable threshold. This enables smarter caching, efficient CI/CD pipelines, and cost savings by ignoring cosmetic or non-semantic edits.
  
  - 🧠 Uses sentence-transformers to embed file contents as latent-meaning vectors
  - 🔍 Compares semantic similarity (cosine) to detect meaningful changes
  - ⚡ Only updates hashes and triggers workflows when semantic drift occurs
  - 🌲 Maintains all Merkle tree benefits, with added semantic awareness
  
  Ideal for: code/documentation monitoring, smart LLM doc generation, policy tracking, and any workflow where *meaning* matters more than bytes.
  
  See README.md for full details, examples, and configuration.
  """
  authors = [
      {name = "octaviusp", email = "octaviopavon7@gmail.com"}
  ]
  license = {text = "MIT"}
  readme = "README.md"
  requires-python = ">=3.12"
  dependencies = [
      "sentence-transformers (>=4.1.0,<5.0.0)",
      "numpy (>=2.2.6,<3.0.0)",
      "tqdm (>=4.67.1,<5.0.0)"
  ]
  
  [build-system]
  requires = ["poetry-core>=2.0.0,<3.0.0"]
  build-backend = "poetry.core.masonry.api"
  [tool.poetry]
  package-mode = false