made local rag needs optional (prevents having to have torch / sentence-transformers if you never use local embedding)

conductor/tech-stack.md

@@ -28,6 +28,7 @@
 - **DeepSeek-V3:** Tier 3 Worker model optimized for code implementation.
 - **DeepSeek-R1:** Specialized reasoning model for complex logical chains and "thinking" traces.
 - **Gemini Embedding 001:** Default embedding model for RAG vector store.
+- **sentence-transformers:** Optional `local-rag` extra for fully local RAG embeddings. Not part of the default install because it pulls in PyTorch.
 
 ## Configuration & Tooling
 
@@ -57,7 +58,7 @@
   - **`/api/ask` Protocol:** Non-blocking, ID-based challenge/response for synchronous HITL approvals from external contexts.
   - **`_predefined_callbacks` and `_gettable_fields`:** AppController-owned registries that the Hook API consumes to expose any App method as a `custom_callback` action.
 
-- **src/rag_engine.py:** Core RAG implementation managing the vector store lifecycle, chunking strategies (character-based and AST-aware), and multi-provider search. Integrates with **ChromaDB** for local persistence and provides a bridge for external MCP retrieval tools.
+- **src/rag_engine.py:** Core RAG implementation managing the vector store lifecycle, chunking strategies (character-based and AST-aware), and multi-provider search. Integrates with **ChromaDB** for local persistence, uses external embeddings by default, and provides an optional local embedding path via `manual_slop[local-rag]`.
 
 - **src/beads_client.py:** Python client for interacting with the [Beads](https://github.com/steveyegge/beads) / Dolt backend. Handles repository initialization, bead creation, status updates, and graph queries.