- glm-ocr is a 1.1B parameter model built specifically for OCR
- No reasoning overhead, no thinking field issues
- Faster inference than qwen3.5 on CPU
- Removed old qwen3.5 models (2B + 0.8B) to free ~4GB disk
- Updated install.sh, ollama.go, .env.example defaults
- ollamaResponse now reads both content and thinking fields
- Falls back to thinking if content is empty
- Install script adds model warm-up prompt to preload into memory
- Warm-up uses OLLAMA_HOST for correct user context
- Makes first real receipt analysis faster
- New provider architecture with common interface
- Provider selected via AI_PROVIDER env var (gemini/openai/ollama)
- Gemini (default): existing implementation, uses GEMINI_API_KEY
- OpenAI-compatible: uses OPENAI_API_KEY + AI_MODEL + AI_BASE_URL
- Works with OpenAI, Perplexity, Together AI, Groq, etc.
- Ollama: local LLM, uses AI_BASE_URL + AI_MODEL
- Supports llava, bakllava, and other vision models
- deepseek.go renamed to llm.go (cleanup)
- .env.example updated with all AI provider options
