Graphify's query mechanism, evolving from keyword-based BFS to embedding-based semantic search.

## 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.