cb34eeb82e
Add a CPU-only, English-first OCR engine (the `ocrs` crate on the `rten` runtime) as an alternative to the local-LLM "vision" backend, which ran too slowly on the target Raspberry Pi 5. - `ANALYSIS_BACKEND` (default `ocr`) selects the engine; `vision` keeps the existing LLM path. Deploy-time, env-only — not admin-tunable. - `analysis::ocr` adds an `OcrEngine` trait (testable seam), the `OcrsEngine` impl (models loaded once, inference on the blocking pool), and an `OcrAnalyzeDispatcher` that plugs into the existing `AnalyzeDispatcher` seam and reuses `persist_analysis`, so OCR text lands in `page_ocr_text` and the `search_doc` tsvector exactly as the vision path produces them. - The backend image bakes the two `.rten` models into /models, where `OCRS_DETECTION_MODEL` / `OCRS_RECOGNITION_MODEL` default. Because `/v1/me/page-search` ranks on `search_doc`, OCR text search works the moment pages are processed. Auto-tagging, scene description and NSFW flags remain the vision backend's job (deferred). Bumps to 0.90.0 (minor) in both manifests. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
15 lines
573 B
Rust
15 lines
573 B
Rust
//! AI content-analysis worker: calls a local OpenAI-compatible vision
|
|
//! model on each page image and turns the result into OCR text, global
|
|
//! auto-tags, a scene description, and NSFW content warnings.
|
|
//!
|
|
//! * [`prompt`] — the system prompt + the bounded output vocabulary and
|
|
//! sanitization helpers (pure, unit-tested).
|
|
//! * [`vision`] — the HTTP client: downscale → request → parse → sanitize.
|
|
//! * [`daemon`] — the job-leasing worker loop (added in the worker phase).
|
|
|
|
pub mod daemon;
|
|
pub mod events;
|
|
pub mod ocr;
|
|
pub mod prompt;
|
|
pub mod vision;
|