feat: semantic query via sentence-transformers embeddings

[juhii31]

Closes #1

What this does

Adds semantic embedding support to graphify query using
sentence-transformers with all-MiniLM-L6-v2 (80MB, local,
no API key required).

Changes

• graphify/embed.py — new module with embed_graph() function

  • Embeds all node labels + docstrings using all-MiniLM-L6-v2
  • Computes pairwise cosine similarity matrix
  • Adds semantically_similar_to edges above configurable
    threshold (default 0.82)
  • Tags edges as INFERRED with confidence_score = cosine similarity
  • Caches embedding vectors in graphify-out/cache/embeddings.json
  • Re-runs only embed new/changed nodes via SHA256 cache key

• tests/test_embed.py — 3 tests covering:

  • Similar nodes get connected
  • Cache file is created on first run
  • No duplicate edges on repeated runs

Notes

• Works fully offline, zero API cost after model download
• All 393 applicable tests passing (symlink and git hook
  tests skipped — Windows privilege limitation, pre-existing)

[juhii31]

This PR adds the embedding engine, caching, and edge creation. Query wiring into main.py and pyproject.toml optional dependency coming in the next commit before merge.

[juhii31]

Updated , query wiring into main.py and pyproject.toml optional dependency now included. graphify query "question" --embeddings uses semantic cosine similarity ranking instead of BFS keyword match. Fully closes #1.