v3: semantic query with embeddings

## Problem Current `/graphify query` is BFS keyword matching - same as grep with graph traversal. Searching "find what handles authentication" only works if the word "auth" appears in node labels. ## Goal Replace keyword BFS with embedding-based semantic search so queries find concepts by meaning, not exact string match. ## Plan **Embedding backend (local by default):** - `sentence-transformers` with `all-MiniLM-L6-v2` (80MB, no API key, works offline) - Optional: OpenAI embeddings API, nomic-embed via ollama **What changes:** - On graph build, embed every node label + source context, store vectors in `graph.json` - `/graphify query` computes query embedding, ranks nodes by cosine similarity, then does BFS from top-k hits - `semantically_similar_to` edge detection can use embeddings instead of LLM (faster, cheaper) - Node similarity surfaced in graph visualization **New optional dependency:** ``` pip install graphifyy[embeddings] ``` ## Why this matters This is the difference between a search tool and an understanding tool. "Find what connects the optimizer to the attention mechanism" should work even if those exact words don't appear together anywhere in the codebase.