v0.7.25 — bundled local AI by default

v0.7.25 — bundled local AI by default

The published Docker image now ships with Qwen2.5-Coder-1.5B-Instruct
baked in as the default AI provider. Bare docker run gets you real
SQL generation, summaries, and infographic narratives — no API key,
no Ollama install, no setup.

This is the change that lets us legitimately call the tool AI-native.

What's bundled

Component	Detail
Model	Qwen2.5-Coder-1.5B-Instruct, Q4_K_M quant (~1 GB on disk)
License	Apache 2.0
Runtime	llama-cpp-python (CPU-only, ~50 MB wheel)
Image size impact	600 MB → ~1.6 GB
RAM at runtime	~1.5 GB resident while loaded
Speed	~30-50 tok/sec CPU → 3-5 s per SQL gen. Apple Silicon Metal / GPU = ~instant
Cold start	~2-3 s mmap at app boot, then every request is warm

Behaviour matrix

Path	Default AI_PROVIDER	Model file location
Docker image	bundled (baked into ENV)	/app/models/qwen2.5-coder-1.5b-instruct-q4_k_m.gguf
pip install rednotebook-ai	mock (no model in wheel)	User points QWEN_MODEL_PATH at their own GGUF
HF Space (after bumping FROM tag)	bundled	Inherits from image

Quality + tradeoff framing

A 1.5B coder model writes plausible SQL on the bundled sample
notebook and clearly-degraded SQL on real warehouses. The admin AI
page now leads with the bundled option and explains the tradeoff in
plain English so users know when to graduate to OpenAI / Anthropic /
a bigger local model via Ollama.

Graceful degradation

Two failure modes, both fall back to mock with a single WARNING log
line — never crash the AI surface:

1. llama_cpp not installed: the side-effect import in
   server/main.py catches it and bundled stays unregistered.
2. GGUF file missing on disk: BundledAIProvider.__init__ raises
   FileNotFoundError; the registry's existing try/except in
   get_provider catches it.

Three new tests cover both paths.

Upgrade

docker pull ghcr.io/sanniheruwala/rednotebook-ai:v0.7.25

HF Space refresh: bump the FROM tag in the Space's Dockerfile to
:v0.7.25. First HF rebuild will be slower than usual (~5-10 min
extra) because the model layer downloads 1 GB from HuggingFace. Every
subsequent app-only release reuses the cached model layer.

Knobs

• AI_PROVIDER=bundled|openai|anthropic|ollama|mock — pick provider.
• QWEN_MODEL_PATH=/path/to/your.gguf — override the bundled GGUF
  (drop in Qwen 7B, Phi-3.5, etc.).
• BUNDLED_AI_THREADS=4 — CPU thread count for inference. Defaults
  to cpu_count - 1.