[gsaaad]

This adds a fully local assistant to DigUp.

It uses a small language model (Qwen2.5-3B or 1.5B via llama.cpp, set up for an 8-core, 12 GB RAM CPU) behind a new digup chat-service helper and an "Ask AI" pane in the app.

Answers are grounded by a deterministic GraphRAG step: indexing stores identifiers, symbols and file references in a new SQLite edges table (created with IF NOT EXISTS, so the schema version is unchanged), and Searcher.retrieveGraphContext follows them one or two hops to pull in related files without any LLM indexing cost.

Cache-Augmented Generation saves the model's state for pinned files to disk, so follow-up questions skip re-reading the context on CPU, and an optional JSONL logger with a secret scrubber lets users export SFT/DPO data and hot-load a LoRA adapter.

Local LLM which maintains content privacy and is completely optional

[gsaaad]

Reopenning as focused different sets of feature implemented... this PR contained all the features I worked on and would like to push.
disregard and closed PR