diff --git a/CLAUDE.md b/CLAUDE.md index d9a113e..3da634f 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -35,13 +35,29 @@ uv run python scripts/build_release.py --quick # Skip web rebuild ### Key Components - **core/capture.py** - CaptureService: Screenshot capture using mss, SSIM-based change detection -- **core/database.py** - SQLite + sqlite-vec for vector similarity search +- **core/database.py** - SQLite + sqlite-vec for vector similarity search (thread-safe with Lock) - **core/embeddings.py** - CLIP model with lazy loading and 5-minute auto-unload -- **core/processor.py** - Background sync service for processing screenshots +- **core/text_embeddings.py** - Text embedding model for OCR semantic search +- **core/ocr.py** - OCR service with pluggable providers (Apple Vision / Tesseract) +- **core/chunking.py** - Dual-size text chunking (small 512 + large 2048 tokens) +- **core/processor.py** - Background sync service for processing screenshots (CLIP + OCR + text embeddings) - **tray/app.py** - TrayApp: System tray menu, spawns backend via subprocess - **tray/backend.py** - Manages FastAPI server as subprocess - **api/main.py** - FastAPI app that serves REST API + static Next.js build from web/out/ +### OCR & Text Search Architecture + +The sync pipeline processes screenshots through: +1. CLIP image embedding → `screenshot_embeddings` table (512-dim vectors) +2. OCR text extraction → `screenshot_ocr` table (full text + confidence) +3. Text chunking → `ocr_text_chunks` table (dual sizes for precision vs context) +4. Text embeddings → `ocr_text_embeddings` table (384-dim vectors) + +Search uses hybrid Reciprocal Rank Fusion combining: +- Image semantic search (CLIP) +- Text fuzzy search (FTS5 trigram) +- Text semantic search (small + large chunks) + ### API Routes Structure Routes in `api/routes/` with prefixes: `/recording`, `/sync`, `/search`, `/screenshots`, `/compression`, `/incognito`, `/setup` @@ -79,6 +95,14 @@ async def get_something(): macOS: `~/Library/Application Support/LiveRecall/` (SQLite database + screenshots folder) +### Database Tables +- `screenshots` - Screenshot metadata (id, path, timestamp, has_embedding, has_ocr) +- `screenshot_embeddings` - CLIP image vectors (512-dim, sqlite-vec) +- `screenshot_ocr` - OCR extracted text (full_text, confidence, word_count) +- `ocr_text_chunks` - Dual-size chunks (small 512 tokens, large 2048 tokens) +- `ocr_text_embeddings` - Text embedding vectors (384-dim, sqlite-vec) +- `ocr_text_fts` - FTS5 full-text search index (trigram tokenizer) + ## API Documentation When running: http://localhost:8742/docs (Swagger) or http://localhost:8742/redoc diff --git a/README.md b/README.md index a01a856..46d5936 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,8 @@ ## Features - **Semantic Search** - Find screenshots by describing what you're looking for +- **OCR Text Extraction** - Automatically extracts text from screenshots for text-based search +- **Hybrid Search** - Combines image similarity and text matching for best results - **Smart Capture** - Only saves when screen content changes - **System Tray App** - Runs quietly in your menu bar - **Web Interface** - Beautiful timeline and search UI @@ -20,7 +22,9 @@ 3. Launch LiveRecall from Applications 4. Grant **Screen Recording** permission when prompted (System Settings > Privacy & Security > Screen Recording) -**Note:** On first launch, the CLIP model (~400MB) will be downloaded automatically. +**Note:** On first launch, required AI models will be downloaded automatically: +- Image embedding model (~400MB) for visual search +- Text embedding model (~130MB) for OCR text search ### Windows / Intel Mac @@ -121,8 +125,27 @@ LiveRecall/ 1. **Capture**: Screenshots are taken at regular intervals when screen content changes 2. **Storage**: Images saved to `~/Library/Application Support/LiveRecall/` (macOS) -3. **Sync**: CLIP model generates embeddings for semantic search (runs on-demand) -4. **Search**: Natural language queries matched against embeddings +3. **Sync**: Processing pipeline generates embeddings and extracts text (runs on-demand) +4. **Search**: Natural language queries matched against image and text content + +### Search Modes + +LiveRecall supports multiple search modes: + +- **Auto (Hybrid)** - Default. Combines all search methods using Reciprocal Rank Fusion for best results +- **Image** - Finds screenshots where the visual content matches your query (e.g., "cat on a keyboard") +- **Text Fuzzy** - Fast text matching with typo tolerance using trigram search +- **Text Semantic** - Finds screenshots containing text with similar meaning to your query + +### OCR Processing + +When sync runs, each screenshot goes through: +1. **Image embedding** - Visual features extracted for semantic image search +2. **OCR extraction** - Text extracted using platform-native OCR (Vision on macOS, Tesseract on Windows) +3. **Text chunking** - Extracted text split into searchable chunks +4. **Text embedding** - Semantic embeddings generated for text search + +You can view extracted text for any screenshot using the "View Text" button in the web UI. ## API Endpoints @@ -131,9 +154,10 @@ LiveRecall/ | `/api/v1/status` | GET | System status | | `/api/v1/recording/start` | POST | Start capture | | `/api/v1/recording/stop` | POST | Stop capture | -| `/api/v1/sync/start` | POST | Start embedding sync | -| `/api/v1/search` | POST | Semantic search | +| `/api/v1/sync/start` | POST | Start embedding/OCR sync | +| `/api/v1/search` | POST | Hybrid search (supports search_mode parameter) | | `/api/v1/screenshots` | GET | List screenshots | +| `/api/v1/screenshots/{id}/ocr` | GET | Get OCR text for a screenshot | Full API docs at http://localhost:8742/docs @@ -146,6 +170,8 @@ Settings available in the web UI or via API: - **Quality**: 50-100% - **Safe Mode**: Filter sensitive content from search - **Auto-compress**: Compress old screenshots +- **OCR Enabled**: Enable/disable text extraction (enabled by default) +- **Search Mode**: auto (hybrid), image, text_fuzzy, text_semantic ## Privacy & Security diff --git a/api/main.py b/api/main.py index a939eb9..6b53715 100644 --- a/api/main.py +++ b/api/main.py @@ -13,7 +13,7 @@ from fastapi.responses import FileResponse, RedirectResponse from fastapi.staticfiles import StaticFiles -from api.routes import compression, incognito, recording, screenshots, search, setup, status, sync +from api.routes import compression, events, incognito, recording, screenshots, search, setup, status, sync from core.database import db @@ -48,6 +48,21 @@ async def lifespan(app: FastAPI): print(f"📁 Database: {db.db_path}") print(f"📊 Screenshots: {stats['total_screenshots']} total, {stats['synced']} synced") + # Auto-start OCR migration if needed (for existing screenshots without OCR) + # This does NOT process new screenshots - those still require manual sync + from core.config import config + from core.processor import processor_service + + if config.ocr.enabled: + try: + ocr_pending = db.get_ocr_pending_count() + if ocr_pending > 0: + print(f"🔄 Starting OCR migration for {ocr_pending} existing screenshots...") + processor_service.start_ocr_migration() + except Exception as e: + # Don't crash the API if OCR migration fails - log and continue + logging.getLogger(__name__).exception("Failed to start OCR migration: %s", e) + yield # Shutdown @@ -122,6 +137,7 @@ async def strip_trailing_slash(request: Request, call_next): app.include_router(compression.router, prefix="/api/v1") app.include_router(incognito.router, prefix="/api/v1") app.include_router(setup.router, prefix="/api/v1") +app.include_router(events.router, prefix="/api/v1") # Serve static web UI if available @@ -195,4 +211,5 @@ async def root(): host="127.0.0.1", port=8742, reload=True, + log_level="warning", # Suppress verbose access logs ) diff --git a/api/routes/events.py b/api/routes/events.py new file mode 100644 index 0000000..f945a1a --- /dev/null +++ b/api/routes/events.py @@ -0,0 +1,314 @@ +""" +SSE Events API Routes +Server-Sent Events for real-time model download and sync progress updates. + +Events: +- model_download: Progress for CLIP/BGE model downloads +- sync_progress: Progress for sync operations (embeddings, OCR, text embeddings) +- model_status: Model loading/unloading status changes +""" + +import asyncio +import contextlib +import json +import logging +import threading +from collections.abc import AsyncGenerator +from contextlib import asynccontextmanager + +from fastapi import APIRouter +from fastapi.responses import StreamingResponse + +from core.database import db + +logger = logging.getLogger(__name__) + +router = APIRouter(prefix="/events", tags=["Events"]) + +# Global event queue for broadcasting events to all connected clients +# Protected by _subscribers_lock for thread-safe access +_event_subscribers: list[asyncio.Queue] = [] +_subscribers_lock = threading.Lock() + + +def broadcast_event(event_type: str, data: dict): + """Broadcast an event to all connected SSE clients. + + Thread-safe: Makes a copy of the subscriber list before iterating + to prevent race conditions with subscribe/unsubscribe operations. + """ + event_data = {"type": event_type, **data} + # Copy list under lock to prevent modification during iteration + with _subscribers_lock: + subscribers = list(_event_subscribers) + for queue in subscribers: + with contextlib.suppress(asyncio.QueueFull): + queue.put_nowait(event_data) + + +@asynccontextmanager +async def subscribe_to_events(): + """Context manager for subscribing to events. + + Thread-safe: Uses lock when modifying the subscriber list. + """ + queue: asyncio.Queue = asyncio.Queue(maxsize=100) + with _subscribers_lock: + _event_subscribers.append(queue) + try: + yield queue + finally: + with _subscribers_lock: + _event_subscribers.remove(queue) + + +async def event_generator() -> AsyncGenerator[str, None]: + """Generate SSE events for connected clients""" + async with subscribe_to_events() as queue: + # Send initial status + yield format_sse_event("connected", {"message": "Connected to event stream"}) + + # Send initial model status + try: + from core.embeddings import get_model_status as get_clip_status + + clip_status = get_clip_status() + yield format_sse_event( + "model_status", + { + "model": "clip", + "loaded": clip_status["loaded"], + "device": clip_status["device"], + }, + ) + except Exception as e: + logger.warning("Error getting CLIP model status: %s", e) + + try: + from core.text_embeddings import get_model_status as get_bge_status + + bge_status = get_bge_status() + yield format_sse_event( + "model_status", + { + "model": "text_embedding", + "loaded": bge_status["loaded"], + "device": bge_status["device"], + }, + ) + except Exception as e: + logger.warning("Error getting text embedding model status: %s", e) + + # Send initial sync status + try: + from core.processor import processor_service + + progress = processor_service.progress + yield format_sse_event( + "sync_progress", + { + "is_syncing": progress.is_running, + "total": progress.total, + "processed": progress.processed, + "errors": progress.errors, + "current_phase": progress.current_phase, + "embeddings_done": progress.embeddings_done, + "ocr_done": progress.ocr_done, + "text_embeddings_done": progress.text_embeddings_done, + }, + ) + except Exception as e: + logger.warning("Error getting sync progress: %s", e) + + # Listen for new events + while True: + try: + # Wait for event with timeout (to send keepalive) + event = await asyncio.wait_for(queue.get(), timeout=30.0) + yield format_sse_event(event["type"], event) + except asyncio.TimeoutError: + # Send keepalive comment + yield ": keepalive\n\n" + + +def format_sse_event(event_type: str, data: dict) -> str: + """Format data as Server-Sent Event""" + return f"event: {event_type}\ndata: {json.dumps(data)}\n\n" + + +@router.get("/stream") +async def event_stream(): + """ + SSE endpoint for real-time events. + + Events sent: + - connected: Initial connection confirmation + - model_status: Model loading/unloading status (clip, text_embedding) + - model_download: Model download progress (progress %, size) + - sync_progress: Sync operation progress (phase, counts) + - ocr_progress: OCR processing progress + """ + return StreamingResponse( + event_generator(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + "X-Accel-Buffering": "no", # Disable nginx buffering + }, + ) + + +@router.get("/status") +async def get_all_status(): + """ + Get current status of all models and sync operations. + + This is a polling alternative to SSE for clients that don't support it. + """ + # CLIP model status + clip_status: dict[str, bool | str | None] = { + "loaded": False, + "device": None, + "downloading": False, + "downloaded": False, + } + try: + from core.embeddings import get_model_status as get_clip_status + + status = get_clip_status() + clip_status = { + "loaded": status["loaded"], + "device": status["device"], + "downloading": False, + "downloaded": status.get("downloaded", False), + } + except Exception as e: + print(f"Error getting CLIP status: {e}") + + # Text embedding model status + text_status: dict[str, bool | str | None] = { + "loaded": False, + "device": None, + "downloading": False, + "downloaded": False, + } + try: + from core.text_embeddings import get_model_status as get_bge_status + + status = get_bge_status() + text_status = { + "loaded": status["loaded"], + "device": status["device"], + "downloading": False, + "downloaded": status.get("downloaded", False), + } + except Exception as e: + print(f"Error getting text embedding status: {e}") + + # OCR status + ocr_status: dict[str, bool | str | None] = {"available": False, "provider": None} + try: + from core.ocr import ocr_service + + ocr_status = { + "available": ocr_service.is_available(), + "provider": ocr_service.get_provider_name() if ocr_service.is_available() else None, + } + except Exception as e: + print(f"Error getting OCR status: {e}") + + # Sync status + sync_status = { + "is_syncing": False, + "total": 0, + "processed": 0, + "current_phase": "", + "embeddings_done": 0, + "ocr_done": 0, + "text_embeddings_done": 0, + } + try: + from core.processor import processor_service + + progress = processor_service.progress + sync_status = { + "is_syncing": progress.is_running, + "total": progress.total, + "processed": progress.processed, + "current_phase": progress.current_phase, + "embeddings_done": progress.embeddings_done, + "ocr_done": progress.ocr_done, + "text_embeddings_done": progress.text_embeddings_done, + } + except Exception as e: + print(f"Error getting sync status: {e}") + + # OCR stats + ocr_stats = {"pending": 0, "completed": 0} + try: + stats = db.get_ocr_stats() + ocr_stats = { + "pending": stats["without_ocr"], + "completed": stats["with_ocr"], + } + except Exception as e: + print(f"Error getting OCR stats: {e}") + + return { + "clip": clip_status, + "text_embedding": text_status, + "ocr": ocr_status, + "sync": sync_status, + "ocr_stats": ocr_stats, + } + + +# Helper function to be called from other modules +def emit_model_download_progress(model: str, progress: float, size_mb: int | None = None): + """Emit model download progress event""" + broadcast_event( + "model_download", + { + "model": model, + "progress": progress, + "size_mb": size_mb, + "status": "downloading" if progress < 100 else "complete", + }, + ) + + +def emit_model_status(model: str, loaded: bool, device: str | None = None): + """Emit model status change event""" + broadcast_event( + "model_status", + { + "model": model, + "loaded": loaded, + "device": device, + }, + ) + + +def emit_sync_progress( + is_syncing: bool, + total: int, + processed: int, + phase: str, + embeddings_done: int = 0, + ocr_done: int = 0, + text_embeddings_done: int = 0, +): + """Emit sync progress event""" + broadcast_event( + "sync_progress", + { + "is_syncing": is_syncing, + "total": total, + "processed": processed, + "current_phase": phase, + "embeddings_done": embeddings_done, + "ocr_done": ocr_done, + "text_embeddings_done": text_embeddings_done, + }, + ) diff --git a/api/routes/screenshots.py b/api/routes/screenshots.py index 89d07ce..02d2f31 100644 --- a/api/routes/screenshots.py +++ b/api/routes/screenshots.py @@ -18,6 +18,7 @@ Screenshot, ScreenshotDeleteResponse, ScreenshotList, + ScreenshotOCRResponse, SuccessResponse, VisibilityFilter, ) @@ -322,6 +323,52 @@ async def get_screenshot_offset( return {"offset": offset} +@router.get("/{screenshot_id}/ocr", response_model=ScreenshotOCRResponse) +async def get_screenshot_ocr(screenshot_id: int): + """ + Get the OCR-extracted text for a screenshot. + + Returns the text that was extracted from this screenshot using OCR, + along with confidence scores and word count. + + - has_ocr: Whether OCR has been processed for this screenshot + - text: The extracted text (empty string if no text found) + - confidence: OCR confidence score (0-1) + - word_count: Number of words in extracted text + """ + # First check if screenshot exists + screenshot = db.get_screenshot(screenshot_id) + if not screenshot: + raise HTTPException(status_code=404, detail="Screenshot not found") + + # Check if OCR has been processed + if not screenshot.get("has_ocr"): + return ScreenshotOCRResponse( + has_ocr=False, + text="", + confidence=None, + word_count=0, + ) + + # Get OCR data + ocr_data = db.get_ocr_text(screenshot_id) + if not ocr_data: + # OCR was marked complete but no data (shouldn't happen, but handle gracefully) + return ScreenshotOCRResponse( + has_ocr=True, + text="", + confidence=None, + word_count=0, + ) + + return ScreenshotOCRResponse( + has_ocr=True, + text=ocr_data.get("full_text", ""), + confidence=ocr_data.get("confidence"), + word_count=ocr_data.get("word_count", 0), + ) + + @router.delete("/{screenshot_id}", response_model=SuccessResponse) async def delete_screenshot(screenshot_id: int, delete_file: bool = True): """ diff --git a/api/routes/search.py b/api/routes/search.py index ebe29a4..cf154b3 100644 --- a/api/routes/search.py +++ b/api/routes/search.py @@ -1,11 +1,21 @@ """ Search API Routes -Semantic search for screenshots +Semantic search for screenshots with hybrid search support. + +Search modes: +- auto: Hybrid search combining image + text (default, recommended) +- image: CLIP image embedding search only +- text_fuzzy: FTS5 trigram text search only +- text_semantic: BGE text embedding search only + +The hybrid search uses Reciprocal Rank Fusion (RRF) to combine results +from multiple sources. """ from fastapi import APIRouter, HTTPException from api.schemas import ( + SearchMode, SearchRequest, SearchResponse, SearchResult, @@ -27,15 +37,18 @@ async def search_screenshots(request: SearchRequest): """ Search for screenshots using natural language. - This loads the CLIP model (if not already loaded) and performs - semantic similarity search against all synced screenshots. + Supports multiple search modes: + - auto: Hybrid search combining image + text (recommended) + - image: CLIP semantic search only + - text_fuzzy: FTS5 trigram text search only + - text_semantic: BGE text embedding search only Optionally filter by date range using start_date and end_date. Examples: - - "blue shirt on amazon" - - "error message in terminal" - - "video call with team" + - "blue shirt on amazon" (visual search) + - "error: connection refused" (text search) + - "video call with team" (hybrid search) """ # Check if we have any synced screenshots stats = db.get_stats() @@ -45,37 +58,95 @@ async def search_screenshots(request: SearchRequest): detail="No synced screenshots. Run sync first to generate embeddings.", ) - # Generate query embedding + # Generate embeddings based on search mode + image_embedding = None + text_embedding = None + try: - if request.safe_mode: - embedding = get_safe_search_embedding( - text=request.query, - safe_mode_level=request.safe_mode_level.value, - ) - elif request.negative_texts: - embedding = get_combined_embedding( - base_text=request.query, - negative_texts=request.negative_texts, - negative_weight=request.negative_weight, - ) - else: - embedding = get_text_embedding(request.query) + # CLIP image embedding (for image and auto modes) + if request.search_mode in (SearchMode.AUTO, SearchMode.IMAGE): + if request.safe_mode: + image_embedding = get_safe_search_embedding( + text=request.query, + safe_mode_level=request.safe_mode_level.value, + ) + elif request.negative_texts: + image_embedding = get_combined_embedding( + base_text=request.query, + negative_texts=request.negative_texts, + negative_weight=request.negative_weight, + ) + else: + image_embedding = get_text_embedding(request.query) + + # BGE text embedding (for text_semantic and auto modes) + if request.search_mode in (SearchMode.AUTO, SearchMode.TEXT_SEMANTIC): + # Lazy import to avoid loading text model if not needed + from core.text_embeddings import text_embedding_service + + text_embedding = text_embedding_service.get_query_embedding(request.query) + except Exception as e: raise HTTPException( status_code=500, detail=f"Error generating embedding: {str(e)}", ) from e - # Search database - get more results than needed if filtering by date + # Search based on mode search_limit = request.limit * 3 if (request.start_date or request.end_date) else request.limit try: - results = db.search_similar( - embedding, - limit=search_limit, - similarity_metric=config.similarity_metric, - visibility=request.visibility.value, - ) + if request.search_mode == SearchMode.IMAGE: + # Pure image search (legacy behavior) + if image_embedding is None: + raise HTTPException(status_code=500, detail="Failed to generate image embedding") + results = db.search_similar( + image_embedding, + limit=search_limit, + similarity_metric=config.similarity_metric, + visibility=request.visibility.value, + ) + # Add empty match_sources for compatibility + for r in results: + r["match_sources"] = ["image"] + + elif request.search_mode == SearchMode.TEXT_FUZZY: + # Pure FTS5 text search + results = db.search_ocr_fts( + query=request.query, + limit=search_limit, + visibility=request.visibility.value, + ) + # Normalize response format + for r in results: + r["similarity"] = r.get("relevance_score", 0.5) # BM25 score as similarity + r["match_sources"] = ["text_fts"] + + elif request.search_mode == SearchMode.TEXT_SEMANTIC: + # Pure BGE text semantic search + if text_embedding is None: + raise HTTPException(status_code=500, detail="Failed to generate text embedding") + results = db.search_text_embeddings( + query_embedding=text_embedding, + limit=search_limit, + visibility=request.visibility.value, + ) + # Normalize response format + for r in results: + r["similarity"] = r.get("text_similarity", 0) + r["match_sources"] = ["text_semantic_small", "text_semantic_large"] + + else: + # Hybrid search (auto mode - default) + results = db.search_hybrid( + query=request.query, + image_embedding=image_embedding, + text_embedding=text_embedding, + mode="auto", + limit=search_limit, + visibility=request.visibility.value, + ) + except Exception as e: raise HTTPException( status_code=500, diff --git a/api/routes/setup.py b/api/routes/setup.py index 01a1a49..3405e15 100644 --- a/api/routes/setup.py +++ b/api/routes/setup.py @@ -3,6 +3,8 @@ Handle first-run and version-change setup flow for screen recording permissions """ +import logging + from fastapi import APIRouter, HTTPException from pydantic import BaseModel @@ -11,6 +13,8 @@ from core.platform import current_platform from core.updater import VERSION +logger = logging.getLogger(__name__) + router = APIRouter(prefix="/setup", tags=["Setup"]) @@ -132,3 +136,186 @@ async def set_autostart(request: AutostartSetRequest): return SuccessResponse(success=True, message="Auto-start disabled.") else: raise HTTPException(status_code=500, detail="Failed to disable auto-start.") + + +# ============================================================================= +# Model Status and Migration +# ============================================================================= + + +@router.get("/models") +async def get_model_status(): + """ + Get status of all ML models (CLIP, BGE, OCR). + + Returns download/loading status for each model to show on setup page. + Status values: "not_downloaded", "downloading", "ready" + """ + # CLIP model status + clip_status = "not_downloaded" + try: + from core.embeddings import get_model_status as get_clip_status + + status = get_clip_status() + # Check downloaded status (model could be downloaded but not loaded) + if status.get("loaded") or status.get("downloaded"): + clip_status = "ready" + except Exception as e: + logger.warning("Error checking CLIP model status: %s", e) + + # Text embedding model status + text_embedding_status = "not_downloaded" + try: + from core.text_embeddings import get_model_status as get_bge_status + + status = get_bge_status() + if status.get("loaded") or status.get("downloaded"): + text_embedding_status = "ready" + except Exception as e: + logger.warning("Error checking text embedding model status: %s", e) + + # OCR status + ocr_status = "not_available" + try: + from core.ocr import ocr_service + + if ocr_service.is_available(): + ocr_status = "ready" + except Exception as e: + logger.warning("Error checking OCR status: %s", e) + + return { + "clip": clip_status, + "text_embedding": text_embedding_status, + "ocr": ocr_status, + "all_ready": clip_status == "ready", # CLIP is required, others are optional + } + + +@router.post("/download-models") +async def download_models(): + """ + Trigger download of required ML models. + + Downloads CLIP and BGE models if not already downloaded. + This will block until models are downloaded. + """ + + from fastapi.responses import StreamingResponse + + async def download_stream(): + """Stream download progress as SSE events""" + import json + + # Download CLIP model + yield f"data: {json.dumps({'model': 'clip', 'status': 'starting'})}\n\n" + try: + from core.embeddings import is_downloaded as clip_is_downloaded + + if not clip_is_downloaded(): + yield f"data: {json.dumps({'model': 'clip', 'status': 'downloading'})}\n\n" + # Import will trigger download + from core.embeddings import get_text_embedding + + # Make a simple call to ensure model is downloaded + _ = get_text_embedding("test") + yield f"data: {json.dumps({'model': 'clip', 'status': 'ready'})}\n\n" + except Exception as e: + yield f"data: {json.dumps({'model': 'clip', 'status': 'error', 'error': str(e)})}\n\n" + + # Download BGE text embedding model + yield f"data: {json.dumps({'model': 'text_embedding', 'status': 'starting'})}\n\n" + try: + from core.text_embeddings import is_downloaded as bge_is_downloaded + + if not bge_is_downloaded(): + yield f"data: {json.dumps({'model': 'text_embedding', 'status': 'downloading'})}\n\n" + from core.text_embeddings import get_text_embedding as get_bge_embedding + + _ = get_bge_embedding("test") + yield f"data: {json.dumps({'model': 'text_embedding', 'status': 'ready'})}\n\n" + except Exception as e: + yield f"data: {json.dumps({'model': 'text_embedding', 'status': 'error', 'error': str(e)})}\n\n" + + yield f"data: {json.dumps({'status': 'complete'})}\n\n" + + return StreamingResponse( + download_stream(), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "Connection": "keep-alive", + }, + ) + + +@router.get("/migration") +async def get_migration_status(): + """ + Get OCR migration status for existing screenshots. + + Returns progress of OCR processing for screenshots that existed + before OCR was enabled. + """ + from api.schemas import MigrationStatus + from core.database import db + + try: + stats = db.get_ocr_stats() + total = stats["total_screenshots"] + with_ocr = stats["with_ocr"] + without_ocr = stats["without_ocr"] + + # Calculate progress + progress_percent = (with_ocr / total * 100) if total > 0 else 100.0 + + # Estimate remaining time (roughly 0.4s per image for OCR + embedding) + estimated_minutes = (without_ocr * 0.4) / 60 if without_ocr > 0 else None + + return MigrationStatus( + needs_migration=without_ocr > 0, + total_screenshots=total, + screenshots_with_ocr=with_ocr, + screenshots_without_ocr=without_ocr, + progress_percent=round(progress_percent, 1), + estimated_time_minutes=round(estimated_minutes, 1) if estimated_minutes else None, + ) + except Exception: + return MigrationStatus( + needs_migration=False, + total_screenshots=0, + screenshots_with_ocr=0, + screenshots_without_ocr=0, + progress_percent=100.0, + estimated_time_minutes=None, + ) + + +@router.get("/enhanced-status") +async def get_enhanced_setup_status(): + """ + Get comprehensive setup status including models and migration. + + Combines version check, model status, and migration progress + for the setup page. + """ + from api.schemas import EnhancedSetupStatus + + # Get model status + models = await get_model_status() + + # Get migration status + migration = await get_migration_status() + + return EnhancedSetupStatus( + current_version=VERSION, + last_seen_version=config.last_seen_version, + needs_setup=config.last_seen_version != VERSION, + platform=current_platform.name, + needs_permission=current_platform.needs_screen_permission(), + models_ready=models["all_ready"], + clip_status=models["clip"], + text_embedding_status=models["text_embedding"], + ocr_status=models["ocr"], + migration_status=migration if migration.needs_migration else None, + ) diff --git a/api/routes/sync.py b/api/routes/sync.py index f4cb1f1..c00aa24 100644 --- a/api/routes/sync.py +++ b/api/routes/sync.py @@ -1,17 +1,29 @@ """ Sync API Routes -Process unsynced screenshots with CLIP embeddings +Process unsynced screenshots with CLIP embeddings + OCR text extraction. + +The sync process has multiple phases: +1. CLIP image embeddings (for visual semantic search) +2. OCR text extraction (for exact/fuzzy text search) +3. BGE text embeddings (for text semantic search) """ -from fastapi import APIRouter, BackgroundTasks +import logging + +from fastapi import APIRouter, BackgroundTasks, HTTPException from api.schemas import ( + AllModelsStatus, + EnhancedSyncStatus, ModelStatus, + OCRConfig, + OCRStats, SuccessResponse, SyncStartRequest, SyncStartResponse, SyncStatus, ) +from core.config import config from core.database import db from core.embeddings import ( get_model_status, @@ -21,6 +33,8 @@ ) from core.processor import SyncProgress, processor_service +logger = logging.getLogger(__name__) + router = APIRouter(prefix="/sync", tags=["Sync"]) @@ -35,6 +49,21 @@ def progress_to_status(progress: SyncProgress) -> SyncStatus: ) +def progress_to_enhanced_status(progress: SyncProgress) -> EnhancedSyncStatus: + """Convert internal progress to enhanced API schema with OCR tracking""" + return EnhancedSyncStatus( + is_syncing=progress.is_running, + total=progress.total, + processed=progress.processed, + errors=progress.errors, + progress_percent=progress.percent, + current_phase=progress.current_phase, + embeddings_done=progress.embeddings_done, + ocr_done=progress.ocr_done, + text_embeddings_done=progress.text_embeddings_done, + ) + + @router.get("", response_model=SyncStatus) @router.get("/status", response_model=SyncStatus) async def get_sync_status(): @@ -50,8 +79,10 @@ async def start_sync( """ Start syncing unsynced screenshots. - This loads the CLIP model (if not already loaded) and generates - embeddings for screenshots that don't have them yet. + This processes screenshots in multiple phases: + 1. CLIP image embeddings (for visual semantic search) + 2. OCR text extraction (for exact/fuzzy text search) + 3. BGE text embeddings (for text semantic search) The sync runs in the background - use /sync/status to monitor progress. """ @@ -62,9 +93,12 @@ async def start_sync( unsynced_count=db.get_unsynced_count(), ) + # Check both CLIP unsynced AND OCR pending unsynced_count = db.get_unsynced_count() + ocr_pending = db.get_ocr_pending_count() if config.ocr.enabled else 0 + total_pending = unsynced_count + ocr_pending - if unsynced_count == 0: + if total_pending == 0: return SyncStartResponse( success=True, message="No screenshots to sync", @@ -78,8 +112,8 @@ async def start_sync( return SyncStartResponse( success=True, - message=f"Sync started for {unsynced_count} screenshots", - unsynced_count=unsynced_count, + message=f"Sync started for {total_pending} screenshots ({unsynced_count} new, {ocr_pending} OCR pending)", + unsynced_count=total_pending, ) @@ -166,3 +200,145 @@ async def set_auto_unload(seconds: int): success=True, message=f"Auto-unload set to {seconds} seconds", ) + + +# ============================================================================= +# OCR Management +# ============================================================================= + + +@router.get("/enhanced", response_model=EnhancedSyncStatus) +async def get_enhanced_sync_status(): + """Get detailed sync status including OCR phase information""" + return progress_to_enhanced_status(processor_service.progress) + + +@router.get("/ocr/status", response_model=OCRStats) +async def get_ocr_status(): + """Get OCR processing statistics + + Returns counts of screenshots with/without OCR, average confidence, etc. + """ + stats = db.get_ocr_stats() + return OCRStats( + total_screenshots=stats["total_screenshots"], + with_ocr=stats["with_ocr"], + without_ocr=stats["without_ocr"], + with_text=stats["with_text"], + avg_confidence=stats["avg_confidence"], + ) + + +@router.get("/ocr/config", response_model=OCRConfig) +async def get_ocr_config(): + """Get current OCR configuration""" + return OCRConfig( + enabled=config.ocr.enabled, + provider=config.ocr.provider, + ) + + +@router.put("/ocr/config", response_model=SuccessResponse) +async def update_ocr_config(new_config: OCRConfig): + """Update OCR configuration + + Note: Changing provider may require reprocessing all screenshots. + Use /sync/ocr/recompute to regenerate OCR data. + """ + config.ocr.enabled = new_config.enabled + config.ocr.provider = new_config.provider + config.save() + + return SuccessResponse( + success=True, + message=f"OCR config updated: enabled={new_config.enabled}, provider={new_config.provider}", + ) + + +@router.post("/ocr/recompute", response_model=SyncStartResponse) +async def recompute_ocr(): + """Recompute OCR for all screenshots + + Use this after changing OCR or text embedding models to regenerate + all text data. This will: + 1. Clear existing OCR data + 2. Re-run OCR on all screenshots + 3. Regenerate text chunks and embeddings + + Warning: This may take a long time for large databases. + """ + if processor_service.is_running: + raise HTTPException( + status_code=409, + detail="Cannot recompute while sync is running", + ) + + if not config.ocr.enabled: + raise HTTPException( + status_code=400, + detail="OCR is disabled. Enable it in settings first.", + ) + + total = db.get_screenshot_count() + + # Start recompute in background + import threading + + def run_recompute(): + try: + processor_service.recompute_ocr() + except Exception as e: + logger.exception("Error during OCR recompute: %s", e) + + thread = threading.Thread(target=run_recompute, daemon=True) + thread.start() + + return SyncStartResponse( + success=True, + message=f"OCR recompute started for {total} screenshots", + unsynced_count=total, + ) + + +@router.get("/models", response_model=AllModelsStatus) +async def get_all_models_status(): + """Get status of all ML models (CLIP, BGE, OCR)""" + # CLIP status + clip_status = get_model_status() + clip = ModelStatus( + loaded=clip_status["loaded"], + device=clip_status["device"], + idle_seconds=clip_status["idle_seconds"], + auto_unload_seconds=clip_status["auto_unload_seconds"], + ) + + # Text embedding status (lazy import) + text_emb = None + try: + from core.text_embeddings import text_embedding_service + + text_status = text_embedding_service.get_model_status() + text_emb = ModelStatus( + loaded=text_status["loaded"], + device=text_status["device"], + idle_seconds=text_status["idle_seconds"], + auto_unload_seconds=text_status["auto_unload_seconds"], + ) + except Exception as e: + logger.warning("Error getting text embedding model status: %s", e) + + # OCR status + ocr_status = "not_available" + try: + from core.ocr import ocr_service + + if ocr_service.is_available(): + ocr_status = ocr_service.get_provider_name() + except Exception as e: + logger.warning("Error getting OCR status: %s", e) + + return AllModelsStatus( + clip=clip, + text_embedding=text_emb, + ocr=ocr_status, + ) diff --git a/api/schemas.py b/api/schemas.py index 228aa8e..b8635c8 100644 --- a/api/schemas.py +++ b/api/schemas.py @@ -165,11 +165,24 @@ class SyncStartResponse(BaseModel): # ============================================================================= +class SearchMode(str, Enum): + """Available search modes""" + + AUTO = "auto" # Hybrid - combines all methods (default) + IMAGE = "image" # CLIP image semantic search only + TEXT_FUZZY = "text_fuzzy" # FTS5 trigram text search only + TEXT_SEMANTIC = "text_semantic" # BGE text embedding search only + + class SearchRequest(BaseModel): - """Search request""" + """Search request with mode selection""" query: str = Field(..., min_length=1, max_length=500) limit: int = Field(default=20, ge=1, le=100) + search_mode: SearchMode = Field( + default=SearchMode.AUTO, + description="Search mode: 'auto' (hybrid), 'image', 'text_fuzzy', 'text_semantic'", + ) safe_mode: bool = Field(default=False) # Off by default for personal recall app safe_mode_level: SafeModeLevel = Field(default=SafeModeLevel.MID) negative_texts: list[str] | None = Field(default=None) @@ -183,6 +196,7 @@ class Config: "example": { "query": "blue shirt on website", "limit": 20, + "search_mode": "auto", "safe_mode": True, "safe_mode_level": "mid", "start_date": "251201000000", @@ -290,6 +304,25 @@ class ScreenshotDeleteResponse(BaseModel): message: str +class ScreenshotOCRResponse(BaseModel): + """Response containing OCR text for a screenshot""" + + has_ocr: bool = Field(description="Whether OCR has been processed for this screenshot") + text: str = Field(default="", description="Extracted text from the screenshot") + confidence: float | None = Field(default=None, description="OCR confidence score (0-1)") + word_count: int = Field(default=0, description="Number of words extracted") + + class Config: + json_schema_extra = { + "example": { + "has_ocr": True, + "text": "Hello World\nThis is extracted text from the screenshot.", + "confidence": 0.95, + "word_count": 8, + } + } + + # ============================================================================= # Bulk Operations # ============================================================================= @@ -469,3 +502,265 @@ class ErrorResponse(BaseModel): success: bool = False error: str detail: str | None = None + + +# ============================================================================= +# OCR Types +# ============================================================================= + + +class OCRResult(BaseModel): + """Result from OCR text extraction""" + + text: str = Field(description="Extracted text (may be empty)") + confidence: float | None = Field(default=None, description="Average confidence 0-1") + word_count: int = Field(default=0, description="Number of words extracted") + language: str = Field(default="en", description="Detected language") + + +class ChunkInfo(BaseModel): + """A single text chunk with position info""" + + text: str + start_char: int + end_char: int + index: int + + +class ChunkedText(BaseModel): + """Result of dual-size chunking""" + + small: list[ChunkInfo] = Field(description="Small chunks (512 tokens)") + large: list[ChunkInfo] = Field(description="Large chunks (2048 tokens)") + + +class OCRStats(BaseModel): + """OCR processing statistics""" + + total_screenshots: int + with_ocr: int = Field(description="Screenshots with OCR processed") + without_ocr: int = Field(description="Screenshots pending OCR") + with_text: int = Field(description="Screenshots with non-empty text") + avg_confidence: float | None = Field(description="Average OCR confidence") + + +# ============================================================================= +# Search Types +# ============================================================================= + + +class MatchSource(str, Enum): + """Source that contributed to a search match""" + + IMAGE = "image" # CLIP image similarity + TEXT_FTS = "text_fts" # FTS5 fuzzy text match + TEXT_SEMANTIC_SMALL = "text_semantic_small" # BGE small chunk match + TEXT_SEMANTIC_LARGE = "text_semantic_large" # BGE large chunk match + + +class EnhancedSearchResult(BaseModel): + """Search result with OCR information""" + + id: int + image_path: str + timestamp: str + similarity: float = Field(description="Combined similarity score 0-1") + image_url: str = Field(description="URL to fetch the image") + match_sources: list[MatchSource] = Field( + default_factory=list, + description="Which search methods matched this result", + ) + ocr_snippet: str | None = Field( + default=None, + description="Highlighted text snippet if text search matched", + ) + + class Config: + json_schema_extra = { + "example": { + "id": 42, + "image_path": "/path/to/screenshot.jpg", + "timestamp": "251206143022", + "similarity": 0.89, + "image_url": "/api/v1/screenshots/42/image", + "match_sources": ["image", "text_fts"], + "ocr_snippet": "...searched **term** found here...", + } + } + + +# ============================================================================= +# Model Download Types +# ============================================================================= + + +class ModelEventType(str, Enum): + """Types of model-related events for SSE""" + + DOWNLOAD_STARTED = "download_started" + DOWNLOAD_PROGRESS = "download_progress" + DOWNLOAD_COMPLETE = "download_complete" + LOADING = "loading" + READY = "ready" + ERROR = "error" + + +class ModelType(str, Enum): + """Types of ML models used""" + + CLIP = "clip" + TEXT_EMBEDDING = "text_embedding" + OCR = "ocr" + + +class ModelEvent(BaseModel): + """SSE event for model download/loading progress""" + + event: ModelEventType + model: ModelType + progress: float | None = Field(default=None, ge=0, le=100, description="Download progress 0-100") + size_mb: int | None = Field(default=None, description="Total size in MB") + message: str | None = Field(default=None, description="Status message") + + +class AllModelsStatus(BaseModel): + """Status of all ML models""" + + clip: ModelStatus + text_embedding: ModelStatus | None = Field( + default=None, + description="Text embedding model status (BGE)", + ) + ocr: str = Field( + default="ready", + description="OCR status: 'ready', 'not_available', provider name", + ) + + +# ============================================================================= +# Enhanced Sync Types +# ============================================================================= + + +class EnhancedSyncStatus(BaseModel): + """Sync status with OCR tracking""" + + is_syncing: bool + total: int = Field(description="Total screenshots to process") + processed: int = Field(description="Screenshots fully processed") + errors: int = Field(description="Number of errors") + progress_percent: float = Field(description="Overall progress percentage") + + # Detailed breakdown + current_phase: str = Field( + default="", + description="Current phase: 'embedding', 'ocr', 'text_embedding'", + ) + embeddings_done: int = Field(default=0, description="CLIP embeddings completed") + ocr_done: int = Field(default=0, description="OCR extractions completed") + text_embeddings_done: int = Field(default=0, description="Text embeddings completed") + + class Config: + json_schema_extra = { + "example": { + "is_syncing": True, + "total": 100, + "processed": 45, + "errors": 2, + "progress_percent": 45.0, + "current_phase": "ocr", + "embeddings_done": 100, + "ocr_done": 45, + "text_embeddings_done": 40, + } + } + + +# ============================================================================= +# OCR Config Types +# ============================================================================= + + +class OCRConfig(BaseModel): + """OCR configuration settings""" + + enabled: bool = Field(default=True, description="Whether OCR is enabled") + provider: str = Field( + default="auto", + description="OCR provider: 'auto', 'apple_vision', 'tesseract'", + ) + + +class TextEmbeddingConfig(BaseModel): + """Text embedding configuration settings""" + + model: str = Field( + default="BAAI/bge-small-en-v1.5", + description="Sentence transformer model name", + ) + dimensions: int = Field(default=384, description="Embedding dimensions") + + +class ChunkingConfig(BaseModel): + """Text chunking configuration settings""" + + small_size: int = Field(default=512, description="Small chunk size in tokens") + small_overlap: int = Field(default=50, description="Small chunk overlap in tokens") + large_size: int = Field(default=2048, description="Large chunk size in tokens") + large_overlap: int = Field(default=200, description="Large chunk overlap in tokens") + + +# ============================================================================= +# Migration Types +# ============================================================================= + + +class MigrationStatus(BaseModel): + """Status of OCR migration for existing screenshots""" + + needs_migration: bool = Field(description="Whether there are screenshots needing OCR") + total_screenshots: int = Field(description="Total number of screenshots") + screenshots_with_ocr: int = Field(description="Screenshots with OCR completed") + screenshots_without_ocr: int = Field(description="Screenshots pending OCR") + progress_percent: float = Field(description="Migration progress percentage") + estimated_time_minutes: float | None = Field( + default=None, + description="Estimated time to complete in minutes", + ) + + class Config: + json_schema_extra = { + "example": { + "needs_migration": True, + "total_screenshots": 1000, + "screenshots_with_ocr": 350, + "screenshots_without_ocr": 650, + "progress_percent": 35.0, + "estimated_time_minutes": 26.0, + } + } + + +class EnhancedSetupStatus(BaseModel): + """Comprehensive setup status including models and migration""" + + # Version info + current_version: str + last_seen_version: str + needs_setup: bool = Field(description="Whether version-based setup is needed") + + # Platform info + platform: str = Field(description="Current platform (macos, windows, linux)") + needs_permission: bool = Field(description="Whether screen permission is needed") + + # Model status + models_ready: bool = Field(description="Whether all required models are ready") + clip_status: str = Field(description="CLIP status: 'not_downloaded', 'downloading', 'ready'") + text_embedding_status: str = Field(description="Text embedding status: 'not_downloaded', 'downloading', 'ready'") + ocr_status: str = Field(description="OCR status: 'ready', 'not_available'") + + # Migration info + migration_status: MigrationStatus | None = Field( + default=None, + description="OCR migration status if applicable", + ) diff --git a/api/tests/test_api.py b/api/tests/test_api.py index 5a2ad83..978cc46 100644 --- a/api/tests/test_api.py +++ b/api/tests/test_api.py @@ -320,6 +320,7 @@ def test_search_success(self, mock_get_embedding, mock_db): "query": "blue shirt", "limit": 10, "safe_mode": False, + "search_mode": "image", # Use image mode to test CLIP-only search }, ) assert response.status_code == 200 @@ -512,3 +513,358 @@ def test_complete_setup(self, mock_config): assert data["success"] is True assert mock_config.last_seen_version == "0.1.2" mock_config.save.assert_called_once() + + +class TestEventsEndpoints: + """Test SSE events API endpoints""" + + @patch("api.routes.events.db") + def test_get_all_status(self, mock_db): + """Should return status of all models and sync""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.events import router + + mock_db.get_ocr_stats.return_value = { + "with_ocr": 50, + "without_ocr": 10, + } + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.get("/events/status") + assert response.status_code == 200 + data = response.json() + assert "clip" in data + assert "text_embedding" in data + assert "ocr" in data + assert "sync" in data + assert "ocr_stats" in data + + +class TestEnhancedSetupEndpoints: + """Test enhanced setup API endpoints""" + + @patch("api.routes.setup.config") + @patch("api.routes.setup.current_platform") + @patch("api.routes.setup.VERSION", "0.1.2") + def test_get_model_status(self, mock_platform, mock_config): + """Should return model status""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.setup import router + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.get("/setup/models") + assert response.status_code == 200 + data = response.json() + assert "clip" in data + assert "text_embedding" in data + assert "ocr" in data + assert "all_ready" in data + + @patch("core.database.db") + @patch("api.routes.setup.config") + @patch("api.routes.setup.current_platform") + @patch("api.routes.setup.VERSION", "0.1.2") + def test_get_migration_status(self, mock_platform, mock_config, mock_db): + """Should return OCR migration status""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.setup import router + + mock_db.get_ocr_stats.return_value = { + "total_screenshots": 100, + "with_ocr": 60, + "without_ocr": 40, + } + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.get("/setup/migration") + assert response.status_code == 200 + data = response.json() + assert data["needs_migration"] is True + assert data["total_screenshots"] == 100 + assert data["screenshots_with_ocr"] == 60 + assert data["screenshots_without_ocr"] == 40 + assert data["progress_percent"] == 60.0 + + @patch("core.database.db") + @patch("api.routes.setup.config") + @patch("api.routes.setup.current_platform") + @patch("api.routes.setup.VERSION", "0.1.2") + def test_get_enhanced_setup_status(self, mock_platform, mock_config, mock_db): + """Should return comprehensive setup status""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.setup import router + + mock_config.last_seen_version = "0.1.1" + mock_platform.name = "macos" + mock_platform.needs_screen_permission.return_value = True + mock_db.get_ocr_stats.return_value = { + "total_screenshots": 100, + "with_ocr": 100, + "without_ocr": 0, + } + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.get("/setup/enhanced-status") + assert response.status_code == 200 + data = response.json() + assert data["current_version"] == "0.1.2" + assert data["needs_setup"] is True + assert data["platform"] == "macos" + assert "clip_status" in data + assert "text_embedding_status" in data + assert "ocr_status" in data + + +class TestSearchModes: + """Test search with different modes""" + + @patch("api.routes.search.db") + @patch("api.routes.search.get_text_embedding") + def test_search_image_mode(self, mock_get_embedding, mock_db): + """Should perform image-only search""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.search import router + + mock_db.get_stats.return_value = {"synced": 10} + mock_get_embedding.return_value = [0.1] * 768 + mock_db.search_similar.return_value = [ + { + "id": 1, + "image_path": "/path/to/img.jpg", + "timestamp": "251206120000", + "similarity": 0.85, + } + ] + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.post( + "/search", + json={ + "query": "test query", + "limit": 10, + "safe_mode": False, + "search_mode": "image", + }, + ) + assert response.status_code == 200 + data = response.json() + assert data["total_results"] == 1 + + @patch("api.routes.search.db") + @patch("api.routes.search.get_text_embedding") + def test_search_text_fuzzy_mode(self, mock_get_embedding, mock_db): + """Should perform text fuzzy search""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.search import router + + mock_db.get_stats.return_value = {"synced": 10} + mock_db.search_text_fts.return_value = [ + { + "id": 1, + "image_path": "/path/to/img.jpg", + "timestamp": "251206120000", + "similarity": 0.9, + } + ] + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.post( + "/search", + json={ + "query": "test query", + "limit": 10, + "safe_mode": False, + "search_mode": "text_fuzzy", + }, + ) + assert response.status_code == 200 + data = response.json() + # Result depends on whether text_fts is implemented + assert "results" in data + + @patch("core.text_embeddings.text_embedding_service") + @patch("api.routes.search.db") + @patch("api.routes.search.get_text_embedding") + def test_search_auto_mode(self, mock_get_embedding, mock_db, mock_text_emb_service): + """Should perform hybrid search in auto mode""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.search import router + + mock_db.get_stats.return_value = {"synced": 10} + mock_get_embedding.return_value = [0.1] * 768 + mock_text_emb_service.get_query_embedding.return_value = [0.1] * 384 + mock_db.search_hybrid.return_value = [ + { + "id": 1, + "image_path": "/path/to/img.jpg", + "timestamp": "251206120000", + "similarity": 0.85, + "match_sources": ["image"], + } + ] + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.post( + "/search", + json={ + "query": "test query", + "limit": 10, + "safe_mode": False, + "search_mode": "auto", + }, + ) + assert response.status_code == 200 + data = response.json() + assert "results" in data + + +class TestScreenshotOCREndpoint: + """Test screenshot OCR endpoint""" + + @patch("api.routes.screenshots.db") + def test_get_ocr_text_success(self, mock_db): + """Should return OCR text for a screenshot with OCR data""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.screenshots import router + + mock_db.get_screenshot.return_value = { + "id": 1, + "image_path": "/path/to/img.jpg", + "timestamp": "251206120000", + "has_embedding": 1, + "has_ocr": 1, + "is_hidden": 0, + } + mock_db.get_ocr_text.return_value = { + "full_text": "Hello world this is sample OCR text", + "confidence": 0.95, + "word_count": 7, + } + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.get("/screenshots/1/ocr") + assert response.status_code == 200 + data = response.json() + assert data["has_ocr"] is True + assert data["text"] == "Hello world this is sample OCR text" + assert data["confidence"] == 0.95 + assert data["word_count"] == 7 + + @patch("api.routes.screenshots.db") + def test_get_ocr_text_not_processed(self, mock_db): + """Should return has_ocr=False when OCR hasn't been processed""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.screenshots import router + + mock_db.get_screenshot.return_value = { + "id": 1, + "image_path": "/path/to/img.jpg", + "timestamp": "251206120000", + "has_embedding": 1, + "has_ocr": 0, + "is_hidden": 0, + } + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.get("/screenshots/1/ocr") + assert response.status_code == 200 + data = response.json() + assert data["has_ocr"] is False + assert data["text"] == "" + assert data["word_count"] == 0 + + @patch("api.routes.screenshots.db") + def test_get_ocr_text_screenshot_not_found(self, mock_db): + """Should return 404 for non-existent screenshot""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.screenshots import router + + mock_db.get_screenshot.return_value = None + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.get("/screenshots/999/ocr") + assert response.status_code == 404 + assert "not found" in response.json()["detail"].lower() + + @patch("api.routes.screenshots.db") + def test_get_ocr_text_empty_text(self, mock_db): + """Should handle screenshots with OCR done but no text extracted""" + from fastapi import FastAPI + from fastapi.testclient import TestClient + + from api.routes.screenshots import router + + mock_db.get_screenshot.return_value = { + "id": 1, + "image_path": "/path/to/img.jpg", + "timestamp": "251206120000", + "has_embedding": 1, + "has_ocr": 1, + "is_hidden": 0, + } + mock_db.get_ocr_text.return_value = { + "full_text": "", + "confidence": None, + "word_count": 0, + } + + app = FastAPI() + app.include_router(router) + client = TestClient(app) + + response = client.get("/screenshots/1/ocr") + assert response.status_code == 200 + data = response.json() + assert data["has_ocr"] is True + assert data["text"] == "" + assert data["word_count"] == 0 diff --git a/core/chunking.py b/core/chunking.py new file mode 100644 index 0000000..06fa9e1 --- /dev/null +++ b/core/chunking.py @@ -0,0 +1,320 @@ +""" +LiveRecall Text Chunking + +Splits OCR text into chunks for semantic search. +Uses dual chunking strategy: small (512 tokens) and large (2048 tokens). + +Small chunks: Precise matching, good for short specific queries +Large chunks: Better context, good for conceptual queries + +Both chunk sizes are searched and combined using RRF (Reciprocal Rank Fusion). +""" + +from __future__ import annotations + +import re +from dataclasses import dataclass + + +@dataclass +class ChunkInfo: + """A single text chunk with position info""" + + text: str + start_char: int + end_char: int + index: int + + +@dataclass +class ChunkedText: + """Result of dual-size chunking""" + + small: list[ChunkInfo] + large: list[ChunkInfo] + + +# Default chunk settings +DEFAULT_SMALL_SIZE = 512 # tokens (~2000 chars) +DEFAULT_SMALL_OVERLAP = 50 # tokens (~200 chars) +DEFAULT_LARGE_SIZE = 2048 # tokens (~8000 chars) +DEFAULT_LARGE_OVERLAP = 200 # tokens (~800 chars) + +# Approximate chars per token (for rough estimation) +CHARS_PER_TOKEN = 4 + +# Minimum chunk size (skip tiny chunks) +MIN_CHUNK_CHARS = 50 + + +def estimate_tokens(text: str) -> int: + """Estimate number of tokens in text (rough approximation)""" + return len(text) // CHARS_PER_TOKEN + + +def create_chunks( + text: str, + chunk_size: int = DEFAULT_SMALL_SIZE, + overlap: int = DEFAULT_SMALL_OVERLAP, + min_chunk_chars: int = MIN_CHUNK_CHARS, +) -> list[ChunkInfo]: + """ + Create chunks from text with overlap. + + Uses paragraph and sentence boundaries when possible for cleaner splits. + + Args: + text: The text to chunk + chunk_size: Target chunk size in tokens + overlap: Overlap between chunks in tokens + min_chunk_chars: Minimum characters for a chunk (skip smaller) + + Returns: + List of ChunkInfo with text and position info + """ + if not text or not text.strip(): + return [] + + text = text.strip() + + # Convert token sizes to character sizes (rough) + chunk_chars = chunk_size * CHARS_PER_TOKEN + overlap_chars = overlap * CHARS_PER_TOKEN + + # If text is small enough, return as single chunk + if len(text) <= chunk_chars: + return [ + ChunkInfo( + text=text, + start_char=0, + end_char=len(text), + index=0, + ) + ] + + chunks: list[ChunkInfo] = [] + + # Split into paragraphs first (preserve structure) + paragraphs = re.split(r"\n\n+", text) + + current_chunk = "" + current_start = 0 + char_position = 0 + chunk_index = 0 + + for para in paragraphs: + para = para.strip() + if not para: + char_position += 2 # Account for newlines + continue + + # If adding this paragraph exceeds chunk size + if len(current_chunk) + len(para) + 2 > chunk_chars: + # Save current chunk if it has content + if current_chunk and len(current_chunk) >= min_chunk_chars: + chunks.append( + ChunkInfo( + text=current_chunk.strip(), + start_char=current_start, + end_char=current_start + len(current_chunk), + index=chunk_index, + ) + ) + chunk_index += 1 + + # Start new chunk with overlap from previous + if overlap_chars > 0 and len(current_chunk) > overlap_chars: + # Get last overlap_chars from current chunk + overlap_text = current_chunk[-overlap_chars:] + # Try to start at a word boundary + space_idx = overlap_text.find(" ") + if space_idx > 0: + overlap_text = overlap_text[space_idx + 1 :] + current_chunk = overlap_text + "\n\n" + current_start = char_position - len(overlap_text) + else: + current_chunk = "" + current_start = char_position + + # Handle oversized paragraph by splitting on sentences + if len(para) > chunk_chars: + para_chunks = _split_paragraph( + para, chunk_chars, overlap_chars, min_chunk_chars, char_position, chunk_index + ) + for pc in para_chunks: + chunks.append(pc) + chunk_index += 1 + + # Reset current chunk + current_chunk = "" + current_start = char_position + len(para) + else: + current_chunk = para + current_start = char_position + else: + # Add paragraph to current chunk + if current_chunk: + current_chunk += "\n\n" + para + else: + current_chunk = para + current_start = char_position + + char_position += len(para) + 2 + + # Don't forget the last chunk + if current_chunk and len(current_chunk) >= min_chunk_chars: + chunks.append( + ChunkInfo( + text=current_chunk.strip(), + start_char=current_start, + end_char=current_start + len(current_chunk), + index=chunk_index, + ) + ) + + return chunks + + +def _split_paragraph( + para: str, + chunk_chars: int, + overlap_chars: int, + min_chunk_chars: int, + base_position: int, + base_index: int, +) -> list[ChunkInfo]: + """Split a large paragraph by sentences""" + # Split on sentence boundaries + sentences = re.split(r"(?<=[.!?])\s+", para) + + chunks: list[ChunkInfo] = [] + current_chunk = "" + current_start = base_position + chunk_index = base_index + char_offset = 0 + + for sentence in sentences: + if not sentence.strip(): + continue + + if len(current_chunk) + len(sentence) + 1 > chunk_chars: + # Save current chunk + if current_chunk and len(current_chunk) >= min_chunk_chars: + chunks.append( + ChunkInfo( + text=current_chunk.strip(), + start_char=current_start, + end_char=current_start + len(current_chunk), + index=chunk_index, + ) + ) + chunk_index += 1 + + # Start new chunk with overlap + if overlap_chars > 0 and len(current_chunk) > overlap_chars: + overlap_text = current_chunk[-overlap_chars:] + space_idx = overlap_text.find(" ") + if space_idx > 0: + overlap_text = overlap_text[space_idx + 1 :] + current_chunk = overlap_text + " " + sentence + current_start = base_position + char_offset - len(overlap_text) + else: + current_chunk = sentence + current_start = base_position + char_offset + else: + if current_chunk: + current_chunk += " " + sentence + else: + current_chunk = sentence + current_start = base_position + char_offset + + char_offset += len(sentence) + 1 + + # Last chunk + if current_chunk and len(current_chunk) >= min_chunk_chars: + chunks.append( + ChunkInfo( + text=current_chunk.strip(), + start_char=current_start, + end_char=current_start + len(current_chunk), + index=chunk_index, + ) + ) + + return chunks + + +def chunk_ocr_text( + full_text: str, + small_size: int = DEFAULT_SMALL_SIZE, + small_overlap: int = DEFAULT_SMALL_OVERLAP, + large_size: int = DEFAULT_LARGE_SIZE, + large_overlap: int = DEFAULT_LARGE_OVERLAP, +) -> ChunkedText: + """ + Create dual-size chunks from OCR text. + + Args: + full_text: The full OCR text to chunk + small_size: Size for small chunks (tokens) + small_overlap: Overlap for small chunks (tokens) + large_size: Size for large chunks (tokens) + large_overlap: Overlap for large chunks (tokens) + + Returns: + ChunkedText with both small and large chunk lists + """ + small_chunks = create_chunks(full_text, small_size, small_overlap) + large_chunks = create_chunks(full_text, large_size, large_overlap) + + return ChunkedText(small=small_chunks, large=large_chunks) + + +def get_chunk_count(text: str, chunk_size: int = DEFAULT_SMALL_SIZE) -> int: + """Estimate number of chunks that will be created""" + if not text: + return 0 + + text_chars = len(text.strip()) + chunk_chars = chunk_size * CHARS_PER_TOKEN + + if text_chars <= chunk_chars: + return 1 + + # Rough estimate accounting for overlap + return max(1, (text_chars // chunk_chars) + 1) + + +if __name__ == "__main__": + # Test chunking + test_text = """ + This is the first paragraph of test text. It contains multiple sentences. + The sentences are meant to test the chunking functionality. + + This is the second paragraph. It's separate from the first one. + We want to make sure paragraph boundaries are respected. + + Here is a third paragraph with more content. The chunking algorithm + should handle this text and split it appropriately based on the + configured chunk size and overlap settings. + + Finally, this is the last paragraph. It wraps up our test text + and provides enough content for meaningful chunk testing. + """ + + print("Testing chunking module...") + print(f"Input text length: {len(test_text)} chars") + print(f"Estimated tokens: {estimate_tokens(test_text)}") + + # Test dual chunking + result = chunk_ocr_text(test_text) + + print(f"\nSmall chunks ({len(result.small)}):") + for chunk in result.small: + print(f" [{chunk.index}] chars {chunk.start_char}-{chunk.end_char}: {chunk.text[:50]}...") + + print(f"\nLarge chunks ({len(result.large)}):") + for chunk in result.large: + print(f" [{chunk.index}] chars {chunk.start_char}-{chunk.end_char}: {chunk.text[:50]}...") + + print("\nDone!") diff --git a/core/config.py b/core/config.py index cd54a5c..d205bc2 100644 --- a/core/config.py +++ b/core/config.py @@ -56,6 +56,69 @@ class CompressionSettings: quality: int = 85 # JPEG quality for compressed images +# ============================================================================= +# OCR SETTINGS +# ============================================================================= + + +@dataclass +class OCRSettings: + """OCR text extraction settings + + Provider options: + - "auto": Auto-select best for platform (recommended) + - "apple_vision": macOS native Vision framework (fast, zero memory) + - "tesseract": Cross-platform Tesseract OCR (lightweight, stable) + + Future provider options (better accuracy, more memory): + - "paddleocr": PP-OCRv4 (pip install paddleocr paddlepaddle) + - "doctr": docTR (pip install python-doctr[torch]) + - "easyocr": EasyOCR (pip install easyocr) - heavy, 1-2GB + + To switch providers: Change provider setting, then call ocr_service.recompute_all() + """ + + enabled: bool = True # OCR enabled by default + provider: str = "auto" # "auto", "apple_vision", "tesseract" + + +@dataclass +class TextEmbeddingSettings: + """Text embedding model settings for semantic search + + Model options (sentence-transformers): + - "BAAI/bge-small-en-v1.5": 384-dim, 130MB, MTEB 62.2 (default, good balance) + - "all-MiniLM-L6-v2": 384-dim, 80MB, MTEB 56.3 (lighter) + - "thenlper/gte-small": 384-dim, 70MB (lightest) + - "BAAI/bge-base-en-v1.5": 768-dim, 440MB, MTEB 63.5 (higher accuracy) + - "all-mpnet-base-v2": 768-dim, 420MB (higher accuracy) + + WARNING: Changing to a model with different dimensions requires: + 1. Update dimensions here + 2. Recreate vector tables (call text_embedding_service.recompute_all()) + """ + + model: str = "BAAI/bge-small-en-v1.5" + dimensions: int = 384 # Must match model output dimensions + + +@dataclass +class ChunkingSettings: + """Text chunking settings for semantic search + + Dual chunking strategy: + - Small chunks: Precise matching, good for short specific queries + - Large chunks: Better context, good for conceptual queries + + Both sizes are searched and combined using RRF (Reciprocal Rank Fusion). + """ + + small_size: int = 512 # tokens (~2000 chars) + small_overlap: int = 50 # tokens (~200 chars) + large_size: int = 2048 # tokens (~8000 chars) + large_overlap: int = 200 # tokens (~800 chars) + + @dataclass class CaptureSettings: """Screen capture settings""" @@ -106,6 +169,9 @@ class Config: capture: CaptureSettings = field(default_factory=CaptureSettings) compression: CompressionSettings = field(default_factory=CompressionSettings) incognito: IncognitoSettings = field(default_factory=IncognitoSettings) + ocr: OCRSettings = field(default_factory=OCRSettings) + text_embedding: TextEmbeddingSettings = field(default_factory=TextEmbeddingSettings) + chunking: ChunkingSettings = field(default_factory=ChunkingSettings) encryption_enabled: bool = True safe_mode_enabled: bool = True safe_mode_level: str = "mid" # low, mid, high @@ -184,6 +250,20 @@ def save(self): "active": self.incognito.active, "until": self.incognito.until, }, + "ocr": { + "enabled": self.ocr.enabled, + "provider": self.ocr.provider, + }, + "text_embedding": { + "model": self.text_embedding.model, + "dimensions": self.text_embedding.dimensions, + }, + "chunking": { + "small_size": self.chunking.small_size, + "small_overlap": self.chunking.small_overlap, + "large_size": self.chunking.large_size, + "large_overlap": self.chunking.large_overlap, + }, "encryption_enabled": self.encryption_enabled, "safe_mode_enabled": self.safe_mode_enabled, "safe_mode_level": self.safe_mode_level, @@ -225,6 +305,26 @@ def load(self): self.incognito.active = inc.get("active", self.incognito.active) self.incognito.until = inc.get("until", self.incognito.until) + # Load OCR settings + if "ocr" in data: + ocr = data["ocr"] + self.ocr.enabled = ocr.get("enabled", self.ocr.enabled) + self.ocr.provider = ocr.get("provider", self.ocr.provider) + + # Load text embedding settings + if "text_embedding" in data: + te = data["text_embedding"] + self.text_embedding.model = te.get("model", self.text_embedding.model) + self.text_embedding.dimensions = te.get("dimensions", self.text_embedding.dimensions) + + # Load chunking settings + if "chunking" in data: + chunk = data["chunking"] + self.chunking.small_size = chunk.get("small_size", self.chunking.small_size) + self.chunking.small_overlap = chunk.get("small_overlap", self.chunking.small_overlap) + self.chunking.large_size = chunk.get("large_size", self.chunking.large_size) + self.chunking.large_overlap = chunk.get("large_overlap", self.chunking.large_overlap) + # Load other settings self.encryption_enabled = data.get("encryption_enabled", self.encryption_enabled) self.safe_mode_enabled = data.get("safe_mode_enabled", self.safe_mode_enabled) diff --git a/core/database.py b/core/database.py index e90b407..8a62800 100644 --- a/core/database.py +++ b/core/database.py @@ -5,6 +5,7 @@ import sqlite3 import struct +import threading import time from contextlib import contextmanager from pathlib import Path @@ -16,6 +17,9 @@ # CLIP ViT-L-14 produces 768-dimensional embeddings EMBEDDING_DIM = 768 +# BGE-small-en-v1.5 produces 384-dimensional text embeddings +TEXT_EMBEDDING_DIM = 384 + def serialize_embedding(embedding: list[float]) -> bytes: """Serialize embedding list to bytes for sqlite-vec""" @@ -33,6 +37,7 @@ class Database: def __init__(self, db_path: Path | None = None): self.db_path = db_path or get_database_path() self.conn: sqlite3.Connection | None = None + self._lock = threading.Lock() # Thread safety for concurrent access def connect(self): """Connect to database and initialize sqlite-vec""" @@ -55,18 +60,19 @@ def disconnect(self): @contextmanager def cursor(self): - """Context manager for cursor""" - if self.conn is None: - raise RuntimeError("Database not connected") - cur = self.conn.cursor() - try: - yield cur - self.conn.commit() - except Exception: - self.conn.rollback() - raise - finally: - cur.close() + """Context manager for cursor with thread-safe locking""" + with self._lock: # Ensure only one thread accesses DB at a time + if self.conn is None: + raise RuntimeError("Database not connected") + cur = self.conn.cursor() + try: + yield cur + self.conn.commit() + except Exception: + self.conn.rollback() + raise + finally: + cur.close() def _initialize_tables(self): """Create tables if they don't exist""" @@ -88,6 +94,9 @@ def _initialize_tables(self): # Migration: add is_hidden column if it doesn't exist self._migrate_hidden_column(cur) + # Migration: add has_ocr column if it doesn't exist + self._migrate_ocr_column(cur) + # Index for faster queries cur.execute(""" CREATE INDEX IF NOT EXISTS idx_screenshots_timestamp @@ -109,7 +118,12 @@ def _initialize_tables(self): ON screenshots(is_hidden) """) - # Virtual table for vector search + cur.execute(""" + CREATE INDEX IF NOT EXISTS idx_screenshots_has_ocr + ON screenshots(has_ocr) + """) + + # Virtual table for vector search (CLIP image embeddings) cur.execute(f""" CREATE VIRTUAL TABLE IF NOT EXISTS screenshot_embeddings USING vec0( @@ -118,6 +132,9 @@ def _initialize_tables(self): ) """) + # OCR tables + self._initialize_ocr_tables(cur) + def _migrate_compression_columns(self, cur): """Add compression columns to existing databases""" # Check if columns exist @@ -141,6 +158,110 @@ def _migrate_hidden_column(self, cur): if "is_hidden" not in columns: cur.execute("ALTER TABLE screenshots ADD COLUMN is_hidden INTEGER DEFAULT 0") + def _migrate_ocr_column(self, cur): + """Add has_ocr column to existing databases""" + cur.execute("PRAGMA table_info(screenshots)") + columns = {row[1] for row in cur.fetchall()} + + if "has_ocr" not in columns: + cur.execute("ALTER TABLE screenshots ADD COLUMN has_ocr INTEGER DEFAULT 0") + + def _initialize_ocr_tables(self, cur): + """Create OCR-related tables if they don't exist""" + # Main OCR text table - stores full extracted text per screenshot + cur.execute(""" + CREATE TABLE IF NOT EXISTS screenshot_ocr ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + screenshot_id INTEGER NOT NULL UNIQUE, + full_text TEXT NOT NULL, + confidence REAL, + word_count INTEGER, + created_at TEXT DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY (screenshot_id) REFERENCES screenshots(id) ON DELETE CASCADE + ) + """) + + cur.execute(""" + CREATE INDEX IF NOT EXISTS idx_screenshot_ocr_screenshot_id + ON screenshot_ocr(screenshot_id) + """) + + # Text chunks table - stores dual-size chunks for semantic search + cur.execute(""" + CREATE TABLE IF NOT EXISTS ocr_text_chunks ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + ocr_id INTEGER NOT NULL, + chunk_size TEXT NOT NULL, + chunk_index INTEGER NOT NULL, + chunk_text TEXT NOT NULL, + start_char INTEGER, + end_char INTEGER, + FOREIGN KEY (ocr_id) REFERENCES screenshot_ocr(id) ON DELETE CASCADE + ) + """) + + cur.execute(""" + CREATE INDEX IF NOT EXISTS idx_ocr_chunks_ocr_id + ON ocr_text_chunks(ocr_id) + """) + + cur.execute(""" + CREATE INDEX IF NOT EXISTS idx_ocr_chunks_size + ON ocr_text_chunks(chunk_size) + """) + + # FTS5 virtual table for fuzzy text search with trigram tokenizer + # Note: tokenize='trigram' requires SQLite 3.34+ + try: + cur.execute(""" + CREATE VIRTUAL TABLE IF NOT EXISTS ocr_text_fts USING fts5( + full_text, + content='screenshot_ocr', + content_rowid='id', + tokenize='trigram' + ) + """) + except sqlite3.OperationalError: + # Fall back to default tokenizer if trigram not available + cur.execute(""" + CREATE VIRTUAL TABLE IF NOT EXISTS ocr_text_fts USING fts5( + full_text, + content='screenshot_ocr', + content_rowid='id' + ) + """) + + # Triggers to keep FTS index in sync with screenshot_ocr table + cur.execute(""" + CREATE TRIGGER IF NOT EXISTS ocr_fts_insert AFTER INSERT ON screenshot_ocr BEGIN + INSERT INTO ocr_text_fts(rowid, full_text) VALUES (new.id, new.full_text); + END + """) + + cur.execute(""" + CREATE TRIGGER IF NOT EXISTS ocr_fts_delete AFTER DELETE ON screenshot_ocr BEGIN + INSERT INTO ocr_text_fts(ocr_text_fts, rowid, full_text) + VALUES ('delete', old.id, old.full_text); + END + """) + + cur.execute(""" + CREATE TRIGGER IF NOT EXISTS ocr_fts_update AFTER UPDATE ON screenshot_ocr BEGIN + INSERT INTO ocr_text_fts(ocr_text_fts, rowid, full_text) + VALUES ('delete', old.id, old.full_text); + INSERT INTO ocr_text_fts(rowid, full_text) VALUES (new.id, new.full_text); + END + """) + + # Vector table for text chunk embeddings (BGE-small: 384 dimensions) + cur.execute(f""" + CREATE VIRTUAL TABLE IF NOT EXISTS ocr_text_embeddings + USING vec0( + chunk_id INTEGER PRIMARY KEY, + embedding float[{TEXT_EMBEDDING_DIM}] + ) + """) + # --- Screenshot CRUD --- def add_screenshot(self, image_path: str, timestamp: str | None = None, is_hidden: bool = False) -> int: @@ -460,6 +581,12 @@ def get_unsynced_count(self) -> int: cur.execute("SELECT COUNT(*) FROM screenshots WHERE has_embedding = 0") return cur.fetchone()[0] + def get_screenshot_count(self) -> int: + """Get total count of all screenshots""" + with self.cursor() as cur: + cur.execute("SELECT COUNT(*) FROM screenshots") + return cur.fetchone()[0] + def add_embedding(self, screenshot_id: int, embedding: list[float]) -> bool: """Add embedding for a screenshot""" if len(embedding) != EMBEDDING_DIM: @@ -618,6 +745,596 @@ def get_compression_stats(self) -> dict: "original_size_bytes": original_total, } + # --- OCR operations --- + + def add_ocr_text( + self, + screenshot_id: int, + full_text: str, + confidence: float | None = None, + word_count: int | None = None, + ) -> int: + """Add OCR extracted text for a screenshot. + + Args: + screenshot_id: ID of the screenshot + full_text: Full extracted text (can be empty string) + confidence: Average OCR confidence (0-1) + word_count: Number of words extracted + + Returns: + ID of the created screenshot_ocr record + """ + if word_count is None: + word_count = len(full_text.split()) if full_text else 0 + + with self.cursor() as cur: + # Use ON CONFLICT DO UPDATE to preserve the id (avoid orphaning chunks) + cur.execute( + """ + INSERT INTO screenshot_ocr (screenshot_id, full_text, confidence, word_count) + VALUES (?, ?, ?, ?) + ON CONFLICT(screenshot_id) DO UPDATE SET + full_text = excluded.full_text, + confidence = excluded.confidence, + word_count = excluded.word_count + """, + (screenshot_id, full_text, confidence, word_count), + ) + # Get the id (either new or existing) + cur.execute( + "SELECT id FROM screenshot_ocr WHERE screenshot_id = ?", + (screenshot_id,), + ) + return cur.fetchone()[0] + + def get_ocr_text(self, screenshot_id: int) -> dict | None: + """Get OCR text for a screenshot""" + with self.cursor() as cur: + cur.execute("SELECT * FROM screenshot_ocr WHERE screenshot_id = ?", (screenshot_id,)) + row = cur.fetchone() + return dict(row) if row else None + + def add_ocr_chunk( + self, + ocr_id: int, + chunk_size: str, + chunk_index: int, + chunk_text: str, + start_char: int | None = None, + end_char: int | None = None, + ) -> int: + """Add a text chunk for an OCR record. + + Args: + ocr_id: ID of the screenshot_ocr record + chunk_size: 'small' or 'large' + chunk_index: Index of this chunk in the sequence + chunk_text: The chunk text + start_char: Starting character position in full text + end_char: Ending character position in full text + + Returns: + ID of the created chunk record + """ + with self.cursor() as cur: + cur.execute( + """ + INSERT INTO ocr_text_chunks (ocr_id, chunk_size, chunk_index, chunk_text, start_char, end_char) + VALUES (?, ?, ?, ?, ?, ?) + """, + (ocr_id, chunk_size, chunk_index, chunk_text, start_char, end_char), + ) + return cur.lastrowid + + def add_text_embedding(self, chunk_id: int, embedding: list[float]) -> bool: + """Add embedding for a text chunk""" + if len(embedding) != TEXT_EMBEDDING_DIM: + raise ValueError(f"Text embedding must be {TEXT_EMBEDDING_DIM} dimensions, got {len(embedding)}") + + embedding_bytes = serialize_embedding(embedding) + + with self.cursor() as cur: + # Use INSERT OR REPLACE for idempotent reprocessing + cur.execute( + "INSERT OR REPLACE INTO ocr_text_embeddings (chunk_id, embedding) VALUES (?, ?)", + (chunk_id, embedding_bytes), + ) + return True + + def mark_ocr_complete(self, screenshot_id: int) -> bool: + """Mark a screenshot as having OCR processed""" + with self.cursor() as cur: + cur.execute("UPDATE screenshots SET has_ocr = 1 WHERE id = ?", (screenshot_id,)) + return cur.rowcount > 0 + + def get_screenshots_without_ocr(self, limit: int | None = None) -> list[dict]: + """Get screenshots that don't have OCR processed yet""" + with self.cursor() as cur: + if limit: + cur.execute( + "SELECT * FROM screenshots WHERE has_ocr = 0 ORDER BY id ASC LIMIT ?", + (limit,), + ) + else: + cur.execute("SELECT * FROM screenshots WHERE has_ocr = 0 ORDER BY id ASC") + return [dict(row) for row in cur.fetchall()] + + def get_ocr_pending_count(self) -> int: + """Get count of screenshots without OCR""" + with self.cursor() as cur: + cur.execute("SELECT COUNT(*) FROM screenshots WHERE has_ocr = 0") + return cur.fetchone()[0] + + def search_ocr_fts( + self, + query: str, + limit: int = 20, + visibility: str = "visible_only", + ) -> list[dict]: + """Search OCR text using FTS5 full-text search. + + Uses trigram matching for fuzzy search if available. + + Args: + query: Search query string + limit: Maximum number of results + visibility: One of "visible_only", "hidden_only", or "all" + + Returns: + List of matching screenshots with OCR info and snippets + """ + # Build visibility condition + if visibility == "visible_only": + vis_condition = "AND (s.is_hidden = 0 OR s.is_hidden IS NULL)" + elif visibility == "hidden_only": + vis_condition = "AND s.is_hidden = 1" + else: + vis_condition = "" + + with self.cursor() as cur: + cur.execute( + f""" + SELECT + s.*, + o.full_text, + o.confidence as ocr_confidence, + o.word_count, + snippet(ocr_text_fts, 0, '', '', '...', 32) as ocr_snippet, + bm25(ocr_text_fts) as relevance_score + FROM ocr_text_fts f + JOIN screenshot_ocr o ON f.rowid = o.id + JOIN screenshots s ON o.screenshot_id = s.id + WHERE ocr_text_fts MATCH ? + {vis_condition} + ORDER BY bm25(ocr_text_fts) + LIMIT ? + """, + (query, limit), + ) + + return [dict(row) for row in cur.fetchall()] + + def search_ocr_exact( + self, + query: str, + limit: int = 20, + visibility: str = "visible_only", + ) -> list[dict]: + """Search OCR text for exact phrase match. + + Args: + query: Exact phrase to search for + limit: Maximum number of results + visibility: One of "visible_only", "hidden_only", or "all" + + Returns: + List of matching screenshots with OCR info + """ + # Build visibility condition + if visibility == "visible_only": + vis_condition = "AND (s.is_hidden = 0 OR s.is_hidden IS NULL)" + elif visibility == "hidden_only": + vis_condition = "AND s.is_hidden = 1" + else: + vis_condition = "" + + # Quote query for exact phrase match in FTS5 + quoted_query = f'"{query}"' + + with self.cursor() as cur: + cur.execute( + f""" + SELECT + s.*, + o.full_text, + o.confidence as ocr_confidence, + o.word_count, + highlight(ocr_text_fts, 0, '', '') as ocr_highlighted + FROM ocr_text_fts f + JOIN screenshot_ocr o ON f.rowid = o.id + JOIN screenshots s ON o.screenshot_id = s.id + WHERE ocr_text_fts MATCH ? + {vis_condition} + ORDER BY s.timestamp DESC + LIMIT ? + """, + (quoted_query, limit), + ) + + return [dict(row) for row in cur.fetchall()] + + def search_text_embeddings( + self, + query_embedding: list[float], + chunk_size: str | None = None, + limit: int = 20, + visibility: str = "visible_only", + ) -> list[dict]: + """Search text chunks by semantic similarity. + + Args: + query_embedding: Query embedding vector (384-dim) + chunk_size: Filter by 'small' or 'large' chunks, or None for all + limit: Maximum number of results + visibility: One of "visible_only", "hidden_only", or "all" + + Returns: + List of matching screenshots with similarity scores + """ + if len(query_embedding) != TEXT_EMBEDDING_DIM: + raise ValueError(f"Query embedding must be {TEXT_EMBEDDING_DIM} dimensions") + + query_bytes = serialize_embedding(query_embedding) + + # Build conditions with parameterized queries to prevent SQL injection + params: list = [query_bytes, limit * 2] + chunk_condition = "" + if chunk_size: + # Validate chunk_size to prevent SQL injection + if chunk_size not in ("small", "large"): + raise ValueError(f"chunk_size must be 'small' or 'large', got '{chunk_size}'") + chunk_condition = "AND c.chunk_size = ?" + params.append(chunk_size) + + if visibility == "visible_only": + vis_condition = "AND (s.is_hidden = 0 OR s.is_hidden IS NULL)" + elif visibility == "hidden_only": + vis_condition = "AND s.is_hidden = 1" + else: + vis_condition = "" + + with self.cursor() as cur: + cur.execute( + f""" + SELECT + s.*, + o.full_text, + o.confidence as ocr_confidence, + c.chunk_text, + c.chunk_size, + c.chunk_index, + e.distance as vec_distance + FROM ocr_text_embeddings e + JOIN ocr_text_chunks c ON c.id = e.chunk_id + JOIN screenshot_ocr o ON o.id = c.ocr_id + JOIN screenshots s ON s.id = o.screenshot_id + WHERE e.embedding MATCH ? + AND k = ? + {chunk_condition} + {vis_condition} + ORDER BY e.distance ASC + """, + params, + ) + + results = [] + seen_screenshots = set() + + for row in cur.fetchall(): + result = dict(row) + screenshot_id = result["id"] + + # Deduplicate by screenshot (keep best match) + if screenshot_id in seen_screenshots: + continue + seen_screenshots.add(screenshot_id) + + # Convert L2 distance to similarity + distance = result.pop("vec_distance", 0) + result["text_similarity"] = max(0.0, min(1.0, 1.0 - (distance**2) / 2)) + + results.append(result) + if len(results) >= limit: + break + + return results + + def search_hybrid( + self, + query: str, + image_embedding: list[float] | None = None, + text_embedding: list[float] | None = None, + mode: str = "auto", + limit: int = 20, + visibility: str = "visible_only", + rrf_k: int = 60, + ) -> list[dict]: + """Hybrid search combining image and text search methods. + + Uses Reciprocal Rank Fusion (RRF) to combine results from: + - CLIP image semantic search + - FTS5 fuzzy text search + - BGE text embedding search (small + large chunks) + + RRF formula: score(d) = Σ (1 / (k + rank(d, q))) + Higher k = more emphasis on top results, lower k = more equal weighting + + Args: + query: The search query string + image_embedding: CLIP image embedding (768-dim) for image search + text_embedding: BGE text embedding (384-dim) for text semantic search + mode: Search mode - "auto", "image", "text_fuzzy", "text_semantic" + limit: Maximum number of results + visibility: One of "visible_only", "hidden_only", or "all" + rrf_k: RRF constant (default 60, standard value) + + Returns: + List of screenshots with combined RRF scores and match sources + """ + # Collect results from each search method + results_by_source: dict[str, list[tuple[int, float]]] = {} + + # Determine which methods to use based on mode + use_image = mode in ("auto", "image") and image_embedding is not None + use_fts = mode in ("auto", "text_fuzzy") + use_text_semantic = mode in ("auto", "text_semantic") and text_embedding is not None + + # 1. CLIP Image Search + if use_image and image_embedding is not None: + try: + image_results = self.search_similar( + query_embedding=image_embedding, + limit=limit * 2, # Get more for RRF merge + visibility=visibility, + ) + results_by_source["image"] = [(r["id"], r.get("similarity", 0)) for r in image_results] + except Exception as e: + print(f"Image search error: {e}") + + # 2. FTS5 Fuzzy Text Search + if use_fts: + try: + fts_results = self.search_ocr_fts( + query=query, + limit=limit * 2, + visibility=visibility, + ) + results_by_source["text_fts"] = [(r["id"], r.get("relevance_score", 0)) for r in fts_results] + except Exception as e: + print(f"FTS search error: {e}") + + # 3. BGE Text Semantic Search (small chunks) + if use_text_semantic and text_embedding is not None: + try: + small_results = self.search_text_embeddings( + query_embedding=text_embedding, + chunk_size="small", + limit=limit * 2, + visibility=visibility, + ) + results_by_source["text_semantic_small"] = [ + (r["id"], r.get("text_similarity", 0)) for r in small_results + ] + except Exception as e: + print(f"Text semantic (small) search error: {e}") + + # 4. BGE Text Semantic Search (large chunks) + if use_text_semantic and text_embedding is not None: + try: + large_results = self.search_text_embeddings( + query_embedding=text_embedding, + chunk_size="large", + limit=limit * 2, + visibility=visibility, + ) + results_by_source["text_semantic_large"] = [ + (r["id"], r.get("text_similarity", 0)) for r in large_results + ] + except Exception as e: + print(f"Text semantic (large) search error: {e}") + + # Apply RRF to combine results + rrf_scores: dict[int, float] = {} + match_sources: dict[int, list[str]] = {} + + for source, ranked_results in results_by_source.items(): + for rank, (screenshot_id, _score) in enumerate(ranked_results, start=1): + # RRF contribution from this source + rrf_contribution = 1.0 / (rrf_k + rank) + rrf_scores[screenshot_id] = rrf_scores.get(screenshot_id, 0) + rrf_contribution + + # Track which sources matched this screenshot + if screenshot_id not in match_sources: + match_sources[screenshot_id] = [] + match_sources[screenshot_id].append(source) + + # Sort by RRF score + sorted_ids = sorted(rrf_scores.keys(), key=lambda x: rrf_scores[x], reverse=True)[:limit] + + # Fetch full screenshot data + results = [] + if sorted_ids: + placeholders = ",".join("?" * len(sorted_ids)) + + if visibility == "visible_only": + vis_condition = "AND (is_hidden = 0 OR is_hidden IS NULL)" + elif visibility == "hidden_only": + vis_condition = "AND is_hidden = 1" + else: + vis_condition = "" + + with self.cursor() as cur: + cur.execute( + f""" + SELECT s.*, o.full_text as ocr_text + FROM screenshots s + LEFT JOIN screenshot_ocr o ON o.screenshot_id = s.id + WHERE s.id IN ({placeholders}) + {vis_condition} + """, + sorted_ids, + ) + + # Build lookup for fetched data + fetched = {row["id"]: dict(row) for row in cur.fetchall()} + + # Combine with scores, maintaining RRF order + for screenshot_id in sorted_ids: + if screenshot_id in fetched: + result = fetched[screenshot_id] + result["similarity"] = rrf_scores[screenshot_id] + result["match_sources"] = match_sources.get(screenshot_id, []) + + # Create snippet if text search matched + if "text_fts" in result["match_sources"] or any( + s.startswith("text_semantic") for s in result["match_sources"] + ): + ocr_text = result.get("ocr_text", "") + if ocr_text: + result["ocr_snippet"] = self._create_snippet(ocr_text, query) + + results.append(result) + + return results + + def _create_snippet(self, text: str, query: str, context_chars: int = 50) -> str: + """Create a text snippet with query highlighted. + + Args: + text: Full text to search in + query: Query to highlight + context_chars: Characters of context around match + + Returns: + Snippet with **highlighted** query match + """ + if not text or not query: + return "" + + # Find best match position (case-insensitive) + text_lower = text.lower() + query_words = query.lower().split() + + # Find first occurrence of any query word + best_pos = len(text) + best_word = "" + for word in query_words: + pos = text_lower.find(word) + if pos != -1 and pos < best_pos: + best_pos = pos + best_word = word + + if best_pos == len(text): + # No match found, return start of text + return text[: context_chars * 2] + "..." if len(text) > context_chars * 2 else text + + # Calculate snippet boundaries + start = max(0, best_pos - context_chars) + end = min(len(text), best_pos + len(best_word) + context_chars) + + # Build snippet + snippet = "" + if start > 0: + snippet += "..." + snippet += text[start:best_pos] + snippet += f"**{text[best_pos : best_pos + len(best_word)]}**" + snippet += text[best_pos + len(best_word) : end] + if end < len(text): + snippet += "..." + + return snippet + + def get_ocr_stats(self) -> dict: + """Get OCR processing statistics""" + with self.cursor() as cur: + cur.execute("SELECT COUNT(*) FROM screenshots WHERE has_ocr = 1") + with_ocr = cur.fetchone()[0] + + cur.execute("SELECT COUNT(*) FROM screenshots WHERE has_ocr = 0") + without_ocr = cur.fetchone()[0] + + cur.execute("SELECT COUNT(*) FROM screenshot_ocr WHERE full_text != ''") + with_text = cur.fetchone()[0] + + cur.execute("SELECT AVG(confidence) FROM screenshot_ocr WHERE confidence IS NOT NULL") + avg_confidence = cur.fetchone()[0] + + cur.execute("SELECT COUNT(*) FROM ocr_text_chunks") + total_chunks = cur.fetchone()[0] + + return { + "total_screenshots": with_ocr + without_ocr, + "with_ocr": with_ocr, + "without_ocr": without_ocr, + "with_text": with_text, + "avg_confidence": avg_confidence, + "total_chunks": total_chunks, + } + + def delete_ocr_data(self, screenshot_id: int) -> bool: + """Delete OCR data for a screenshot (for reprocessing)""" + with self.cursor() as cur: + # Get OCR record ID + cur.execute("SELECT id FROM screenshot_ocr WHERE screenshot_id = ?", (screenshot_id,)) + row = cur.fetchone() + if not row: + return False + + ocr_id = row[0] + + # Get chunk IDs for this OCR record + cur.execute("SELECT id FROM ocr_text_chunks WHERE ocr_id = ?", (ocr_id,)) + chunk_ids = [r[0] for r in cur.fetchall()] + + # Delete text embeddings + if chunk_ids: + placeholders = ",".join("?" * len(chunk_ids)) + cur.execute(f"DELETE FROM ocr_text_embeddings WHERE chunk_id IN ({placeholders})", chunk_ids) + + # Delete chunks + cur.execute("DELETE FROM ocr_text_chunks WHERE ocr_id = ?", (ocr_id,)) + + # Delete OCR record (FTS will auto-sync via trigger) + cur.execute("DELETE FROM screenshot_ocr WHERE id = ?", (ocr_id,)) + + # Reset has_ocr flag + cur.execute("UPDATE screenshots SET has_ocr = 0 WHERE id = ?", (screenshot_id,)) + + return True + + def reset_all_ocr(self) -> int: + """Reset all OCR data for reprocessing with new provider. + + Returns: + Number of screenshots that will need reprocessing + """ + with self.cursor() as cur: + # Delete all text embeddings + cur.execute("DELETE FROM ocr_text_embeddings") + + # Delete all chunks + cur.execute("DELETE FROM ocr_text_chunks") + + # Delete all OCR records + cur.execute("DELETE FROM screenshot_ocr") + + # Reset all has_ocr flags + cur.execute("UPDATE screenshots SET has_ocr = 0") + count = cur.rowcount + + return count + # --- Stats --- def get_stats(self) -> dict: diff --git a/core/embeddings.py b/core/embeddings.py index 243a69f..c5301a2 100644 --- a/core/embeddings.py +++ b/core/embeddings.py @@ -131,10 +131,30 @@ def is_loaded() -> bool: return _model is not None +def is_downloaded() -> bool: + """Check if the CLIP model is downloaded in HuggingFace cache""" + try: + from huggingface_hub import try_to_load_from_cache + + # clip-ViT-L-14 maps to sentence-transformers/clip-ViT-L-14 + model_id = "sentence-transformers/clip-ViT-L-14" + + # Check for the sentence-transformers config file + result = try_to_load_from_cache(model_id, "config_sentence_transformers.json") + if result is not None: + return True + # Fallback: some caches/models may use config.json + result = try_to_load_from_cache(model_id, "config.json") + return result is not None + except Exception: + return False + + def get_model_status() -> dict: """Get detailed model status""" return { "loaded": _model is not None, + "downloaded": is_downloaded(), "device": _device, "last_used": _last_used, "idle_seconds": time.time() - _last_used if _last_used > 0 else 0, diff --git a/core/ocr.py b/core/ocr.py new file mode 100644 index 0000000..ff39751 --- /dev/null +++ b/core/ocr.py @@ -0,0 +1,251 @@ +""" +LiveRecall OCR Service +Abstraction layer for OCR text extraction with pluggable providers + +============================================================================= +OCR PROVIDER OPTIONS +============================================================================= + +Current: Apple Vision (macOS) / Tesseract (Windows) + - Apple Vision: Native macOS, zero memory overhead, fast (~200ms), excellent on UI text + - Tesseract: Cross-platform, lightweight (~50MB), stable + +Upgrade options (better accuracy): + - PaddleOCR PP-OCRv4: pip install paddleocr paddlepaddle + Best balance of accuracy/speed, complex macOS install + - docTR: pip install python-doctr[torch] + Good accuracy, clean install, Apache 2.0 + - EasyOCR: pip install easyocr + Easy install but heavy (1-2GB memory) + +High-end (if hardware allows): + - GOT-OCR2: State-of-the-art, 8GB+ VRAM required + - Surya: Excellent accuracy but GPL license (problematic for MIT projects) + +To switch provider: + 1. Update config: config.ocr.provider = "paddleocr" + 2. Run: ocr_service.recompute_all_ocr() +============================================================================= +""" + +from __future__ import annotations + +import platform +import threading +from dataclasses import dataclass +from typing import TYPE_CHECKING, Protocol, runtime_checkable + +if TYPE_CHECKING: + from pathlib import Path + + +@dataclass +class OCRResult: + """Result from OCR text extraction""" + + text: str + confidence: float | None = None + word_count: int = 0 + language: str = "en" + + @classmethod + def empty(cls) -> OCRResult: + """Create an empty OCR result for images with no text""" + return cls(text="", confidence=None, word_count=0) + + +@runtime_checkable +class OCRProvider(Protocol): + """ + Abstract interface for OCR engines. + + Implement this protocol to add a new OCR provider. + See core/ocr_providers/ for example implementations. + """ + + name: str + + def extract_text(self, image_path: str | Path) -> OCRResult: + """Extract text from an image file""" + ... + + def is_available(self) -> bool: + """Check if this provider is available on the current system""" + ... + + def get_model_info(self) -> dict: + """Get information about the OCR model/engine""" + ... + + +class OCRService: + """ + OCR service with pluggable providers. + + Supports automatic provider selection based on platform and config. + Providers are registered and can be switched at runtime. + """ + + _providers: dict[str, type[OCRProvider]] = {} + + def __init__(self): + self._current_provider: OCRProvider | None = None + self._provider_cache: dict[str, OCRProvider] = {} + self._lock = threading.Lock() + + @classmethod + def register(cls, name: str, provider_class: type[OCRProvider]) -> None: + """Register an OCR provider""" + cls._providers[name] = provider_class + + @classmethod + def get_available_providers(cls) -> list[str]: + """Get list of registered provider names""" + return list(cls._providers.keys()) + + def get_provider(self, provider_name: str | None = None) -> OCRProvider: + """ + Get an OCR provider instance. + + Args: + provider_name: Specific provider to use. If None or "auto", + automatically selects based on platform. + """ + if provider_name is None or provider_name == "auto": + provider_name = self._get_default_provider_name() + + if provider_name not in self._providers: + available = ", ".join(self._providers.keys()) + raise ValueError(f"Unknown OCR provider: {provider_name}. Available: {available}") + + # Check cache first (fast path without lock) + if provider_name in self._provider_cache: + return self._provider_cache[provider_name] + + # Double-checked locking for thread-safe initialization + with self._lock: + # Re-check inside lock in case another thread initialized + if provider_name in self._provider_cache: + return self._provider_cache[provider_name] + + provider_class = self._providers[provider_name] + provider = provider_class() + + if not provider.is_available(): + raise RuntimeError(f"OCR provider '{provider_name}' is not available on this system") + + # Cache for reuse + self._provider_cache[provider_name] = provider + return provider + + def _get_default_provider_name(self) -> str: + """Get the default provider name based on platform""" + system = platform.system() + + if system == "Darwin" and "apple_vision" in self._providers: + return "apple_vision" + elif system == "Windows" and "tesseract" in self._providers: + return "tesseract" + + # Fallback to tesseract if available + if "tesseract" in self._providers: + return "tesseract" + + # Return first available provider + if self._providers: + return next(iter(self._providers.keys())) + + raise RuntimeError("No OCR providers registered") + + def extract_text(self, image_path: str | Path, provider_name: str | None = None) -> OCRResult: + """ + Extract text from an image. + + Args: + image_path: Path to the image file + provider_name: Specific provider to use (None for auto) + + Returns: + OCRResult with extracted text and metadata + """ + provider = self.get_provider(provider_name) + return provider.extract_text(image_path) + + def get_current_provider_info(self, provider_name: str | None = None) -> dict: + """Get information about the current/specified provider""" + provider = self.get_provider(provider_name) + return { + "name": provider.name, + "available": provider.is_available(), + **provider.get_model_info(), + } + + def is_available(self) -> bool: + """Check if any OCR provider is available""" + try: + self.get_provider() + return True + except (ValueError, RuntimeError): + return False + + def get_provider_name(self) -> str: + """Get the name of the current/default provider""" + try: + provider = self.get_provider() + return provider.name + except (ValueError, RuntimeError): + return "none" + + +# Global OCR service instance +ocr_service = OCRService() + + +def _register_providers(): + """Register available OCR providers""" + # Import and register Apple Vision provider (macOS only) + if platform.system() == "Darwin": + try: + from core.ocr_providers.apple_vision import AppleVisionOCR + + OCRService.register("apple_vision", AppleVisionOCR) + except ImportError: + pass # ocrmac not installed + + # Import and register Tesseract provider (cross-platform) + try: + from core.ocr_providers.tesseract import TesseractOCR + + OCRService.register("tesseract", TesseractOCR) + except ImportError: + pass # pytesseract not installed + + +# Register providers on module load +_register_providers() + + +# Convenience functions +def extract_text(image_path: str | Path, provider_name: str | None = None) -> OCRResult: + """Extract text from an image (convenience function)""" + return ocr_service.extract_text(image_path, provider_name) + + +def get_ocr_provider_info(provider_name: str | None = None) -> dict: + """Get OCR provider information (convenience function)""" + return ocr_service.get_current_provider_info(provider_name) + + +def get_available_providers() -> list[str]: + """Get list of available OCR providers""" + return OCRService.get_available_providers() + + +if __name__ == "__main__": + print("OCR Service Test") + print(f"Platform: {platform.system()}") + print(f"Available providers: {get_available_providers()}") + + if get_available_providers(): + info = get_ocr_provider_info() + print(f"Default provider info: {info}") diff --git a/core/ocr_providers/__init__.py b/core/ocr_providers/__init__.py new file mode 100644 index 0000000..5b491de --- /dev/null +++ b/core/ocr_providers/__init__.py @@ -0,0 +1,20 @@ +""" +LiveRecall OCR Providers + +This package contains OCR provider implementations. +Each provider implements the OCRProvider protocol from core.ocr. + +Available providers: +- apple_vision: Native macOS Vision framework (macOS only) +- tesseract: Tesseract OCR engine (cross-platform) + +To add a new provider: +1. Create a new file in this directory (e.g., paddleocr.py) +2. Implement the OCRProvider protocol +3. Register it in core/ocr.py _register_providers() +""" + +from __future__ import annotations + +# Providers are imported conditionally in core/ocr.py based on availability +__all__: list[str] = [] diff --git a/core/ocr_providers/apple_vision.py b/core/ocr_providers/apple_vision.py new file mode 100644 index 0000000..c586e09 --- /dev/null +++ b/core/ocr_providers/apple_vision.py @@ -0,0 +1,142 @@ +""" +Apple Vision OCR Provider + +Uses macOS native Vision framework via the ocrmac library. +This is the recommended provider for macOS due to: +- Native Apple Silicon optimization (Neural Engine) +- Zero additional memory overhead (uses system APIs) +- Fast processing (~200ms per image) +- Excellent accuracy on UI text and screen content + +Requirements: +- macOS 10.15+ (Catalina or later) +- pip install ocrmac +""" + +from __future__ import annotations + +import platform +import threading +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from ocrmac.ocrmac import OCR as OCRType + + from core.ocr import OCRResult + +# Module-level singleton for OCR class (following codebase conventions) +_ocr_class: type[OCRType] | None = None +_ocr_class_lock = threading.Lock() + + +def _get_ocr_class() -> type[OCRType]: + """Get the OCR class, loading it if necessary (module-level singleton)""" + global _ocr_class + # Fast path: check without lock + if _ocr_class is not None: + return _ocr_class + + # Double-checked locking for thread-safe initialization + with _ocr_class_lock: + # Re-check inside lock + if _ocr_class is None: + try: + from ocrmac.ocrmac import OCR + + _ocr_class = OCR + except ImportError as e: + raise ImportError("ocrmac is required for Apple Vision OCR. Install it with: pip install ocrmac") from e + return _ocr_class + + +class AppleVisionOCR: + """ + OCR provider using Apple's Vision framework. + + This provider is only available on macOS and uses the native + Vision framework for text recognition, which provides: + - Hardware acceleration on Apple Silicon + - No additional model downloads required + - Excellent accuracy for screen content + """ + + name = "apple_vision" + + def extract_text(self, image_path: str | Path) -> OCRResult: + """ + Extract text from an image using Apple Vision. + + Args: + image_path: Path to the image file + + Returns: + OCRResult with extracted text and confidence + """ + from core.ocr import OCRResult + + image_path = Path(image_path) + if not image_path.exists(): + raise FileNotFoundError(f"Image not found: {image_path}") + + OCR = _get_ocr_class() + + try: + # ocrmac returns list of (text, confidence, bbox) tuples + results = OCR(str(image_path)).recognize() + + if not results: + return OCRResult.empty() + + # Combine all text with newlines between blocks + texts = [] + confidences = [] + + for item in results: + if len(item) >= 2: + text, confidence = item[0], item[1] + if text and text.strip(): + texts.append(text.strip()) + if confidence is not None: + confidences.append(confidence) + + full_text = "\n".join(texts) + avg_confidence = sum(confidences) / len(confidences) if confidences else None + word_count = len(full_text.split()) if full_text else 0 + + return OCRResult( + text=full_text, + confidence=avg_confidence, + word_count=word_count, + language="en", # Vision framework auto-detects but we default to en + ) + + except Exception as e: + print(f"Apple Vision OCR error: {e}") + # Return empty result on error rather than raising + return OCRResult.empty() + + def is_available(self) -> bool: + """Check if Apple Vision OCR is available""" + if platform.system() != "Darwin": + return False + + try: + from ocrmac.ocrmac import OCR # noqa: F401 + + return True + except ImportError: + return False + + def get_model_info(self) -> dict: + """Get information about the Vision framework""" + import platform as plat + + return { + "engine": "Apple Vision Framework", + "platform": "macOS", + "macos_version": plat.mac_ver()[0] if plat.system() == "Darwin" else None, + "requires_download": False, + "memory_overhead": "minimal (uses system APIs)", + "supported_languages": ["en", "zh", "ja", "ko", "de", "fr", "it", "pt", "es"], + } diff --git a/core/ocr_providers/tesseract.py b/core/ocr_providers/tesseract.py new file mode 100644 index 0000000..61a304d --- /dev/null +++ b/core/ocr_providers/tesseract.py @@ -0,0 +1,156 @@ +""" +Tesseract OCR Provider + +Uses Tesseract OCR engine via pytesseract library. +This is the recommended provider for Windows and as a cross-platform fallback. + +Advantages: +- Cross-platform (Windows, macOS, Linux) +- Lightweight (~50MB memory) +- Fast processing (~100ms per image) +- Well-tested and stable + +Requirements: +- Tesseract binary installed on system: + - Windows: choco install tesseract OR download from https://github.com/UB-Mannheim/tesseract/wiki + - macOS: brew install tesseract + - Linux: apt install tesseract-ocr +- pip install pytesseract +""" + +from __future__ import annotations + +import shutil +from pathlib import Path +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from core.ocr import OCRResult + + +class TesseractOCR: + """ + OCR provider using Tesseract OCR engine. + + This provider works cross-platform and is the default + for Windows systems. It requires the Tesseract binary + to be installed on the system. + """ + + name = "tesseract" + + def __init__(self): + self._pytesseract = None + + def _get_pytesseract(self): + """Lazy load pytesseract""" + if self._pytesseract is None: + try: + import pytesseract + + self._pytesseract = pytesseract + except ImportError as e: + raise ImportError( + "pytesseract is required for Tesseract OCR. Install it with: pip install pytesseract" + ) from e + return self._pytesseract + + def extract_text(self, image_path: str | Path) -> OCRResult: + """ + Extract text from an image using Tesseract. + + Args: + image_path: Path to the image file + + Returns: + OCRResult with extracted text and confidence + """ + from core.ocr import OCRResult + + image_path = Path(image_path) + if not image_path.exists(): + raise FileNotFoundError(f"Image not found: {image_path}") + + pytesseract = self._get_pytesseract() + + try: + from PIL import Image + + image = Image.open(image_path) + + # Get detailed OCR data including confidence scores + data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT) + + # Filter and collect text with confidence + words = [] + confidences = [] + + for i, conf in enumerate(data["conf"]): + # Tesseract returns -1 for non-text elements + if int(conf) > 0: + text = data["text"][i] + if text and text.strip(): + words.append(text.strip()) + confidences.append(int(conf)) + + # Join words into full text + full_text = " ".join(words) + + # Calculate average confidence (Tesseract uses 0-100 scale) + avg_confidence = sum(confidences) / len(confidences) / 100.0 if confidences else None + + return OCRResult( + text=full_text, + confidence=avg_confidence, + word_count=len(words), + language="en", + ) + + except Exception as e: + print(f"Tesseract OCR error: {e}") + return OCRResult.empty() + + def is_available(self) -> bool: + """Check if Tesseract is available""" + # Check if pytesseract is installed + try: + import pytesseract # noqa: F401 + except ImportError: + return False + + # Check if tesseract binary is available + tesseract_cmd = shutil.which("tesseract") + return tesseract_cmd is not None + + def get_model_info(self) -> dict: + """Get information about the Tesseract installation""" + tesseract_path = shutil.which("tesseract") + version = None + + if tesseract_path: + try: + import subprocess + + result = subprocess.run([tesseract_path, "--version"], capture_output=True, text=True, timeout=5) + # First line contains version + version = result.stdout.split("\n")[0] if result.stdout else None + except Exception: + pass + + return { + "engine": "Tesseract OCR", + "binary_path": tesseract_path, + "version": version, + "requires_download": True, + "memory_overhead": "~50MB", + "supported_languages": self._get_installed_languages(), + } + + def _get_installed_languages(self) -> list[str]: + """Get list of installed Tesseract language packs""" + try: + pytesseract = self._get_pytesseract() + langs = pytesseract.get_languages() + return langs if langs else ["eng"] + except Exception: + return ["eng"] # Default assumption diff --git a/core/processor.py b/core/processor.py index a9d7f5e..9fef8bd 100644 --- a/core/processor.py +++ b/core/processor.py @@ -1,25 +1,57 @@ """ LiveRecall Processor -Sync service for generating embeddings for unsynced screenshots +Sync service for generating embeddings for unsynced screenshots. + +Supports multiple processing phases: +1. CLIP image embeddings (visual semantic search) +2. OCR text extraction (exact/fuzzy text search) +3. BGE text embeddings (text semantic search) + +OCR and text embedding providers can be configured in config.py. +To change models, update config and call processor_service.recompute_ocr(). """ +import contextlib import threading from collections.abc import Callable from dataclasses import dataclass +from core.config import config from core.database import db from core.embeddings import get_image_embedding +class OCRProcessingError(Exception): + """Raised when OCR processing fails for expected reasons (corrupted image, etc.)""" + + pass + + @dataclass class SyncProgress: - """Progress information for sync operation""" + """Progress information for sync operation with detailed phase tracking + + Phases: + 1. embedding: Generate CLIP image embeddings + 2. ocr: Extract text via OCR + 3. text_embedding: Generate BGE text embeddings for chunks + """ total: int = 0 processed: int = 0 errors: int = 0 is_running: bool = False + # Detailed phase tracking + current_phase: str = "" # "embedding", "ocr", "text_embedding" + embeddings_done: int = 0 + ocr_done: int = 0 + text_embeddings_done: int = 0 + + # Error tracking for visibility + last_error: str = "" + error_type: str = "" # "ocr_failed", "db_error", "code_error" + @property def remaining(self) -> int: return self.total - self.processed - self.errors @@ -32,7 +64,22 @@ def percent(self) -> float: class ProcessorService: - """Service for syncing screenshots (generating embeddings)""" + """Service for syncing screenshots (embeddings + OCR + text embeddings) + + Processing pipeline: + 1. Generate CLIP image embeddings for unsynced screenshots + 2. Run OCR on screenshots without OCR data (if enabled) + 3. Generate BGE text embeddings for OCR text chunks + + Configuration: + - OCR provider: config.ocr.provider ("auto", "apple_vision", "tesseract") + - Text model: config.text_embedding.model (default: "BAAI/bge-small-en-v1.5") + - Chunk sizes: config.chunking.small_size, config.chunking.large_size + + To change OCR/embedding models: + 1. Update config settings + 2. Call processor_service.recompute_ocr() to reprocess all screenshots + """ def __init__(self): self._running = False @@ -41,6 +88,35 @@ def __init__(self): self._on_progress: Callable[[SyncProgress], None] | None = None self._cancel_requested = False + # Lazy-loaded services (imported on first use to avoid circular imports) + self._ocr_service = None + self._text_embedding_service = None + self._chunking_module = None + + def _get_ocr_service(self): + """Lazy load OCR service""" + if self._ocr_service is None: + from core.ocr import ocr_service + + self._ocr_service = ocr_service + return self._ocr_service + + def _get_text_embedding_service(self): + """Lazy load text embedding service""" + if self._text_embedding_service is None: + from core.text_embeddings import text_embedding_service + + self._text_embedding_service = text_embedding_service + return self._text_embedding_service + + def _get_chunking(self): + """Lazy load chunking module""" + if self._chunking_module is None: + from core import chunking + + self._chunking_module = chunking + return self._chunking_module + @property def is_running(self) -> bool: return self._running @@ -60,6 +136,49 @@ def start(self, batch_size: int = 10, on_progress: Callable[[SyncProgress], None self._thread = threading.Thread(target=self._process_loop, args=(batch_size,), daemon=True) self._thread.start() + def start_ocr_migration(self, batch_size: int = 10, on_progress: Callable[[SyncProgress], None] | None = None): + """Start OCR migration for screenshots that have CLIP but no OCR. + + This only processes existing screenshots that need OCR migration, + NOT new screenshots that haven't been synced yet. + Use start() for full sync including new screenshots. + """ + if self._running: + return + + # Only start if there are OCR-pending screenshots + ocr_pending = db.get_ocr_pending_count() if config.ocr.enabled else 0 + if ocr_pending == 0: + return + + self._running = True + self._cancel_requested = False + self._on_progress = on_progress + self._thread = threading.Thread(target=self._ocr_migration_loop, args=(batch_size,), daemon=True) + self._thread.start() + + def _ocr_migration_loop(self, batch_size: int): + """Background loop for OCR migration only (no CLIP processing)""" + ocr_pending = db.get_ocr_pending_count() if config.ocr.enabled else 0 + + if ocr_pending == 0: + self._running = False + self._progress = SyncProgress(total=0, processed=0, errors=0, is_running=False) + return + + self._progress = SyncProgress(total=ocr_pending, processed=0, errors=0, is_running=True) + self._progress.current_phase = "ocr" + + print(f"🔄 Auto-syncing OCR for {ocr_pending} existing screenshots...") + + # Only process OCR migration (skip CLIP phase) + self._process_pending_ocr(batch_size) + + # Mark as complete + self._running = False + self._progress.is_running = False + print(f"✅ OCR migration complete: {self._progress.processed} processed, {self._progress.errors} errors") + def stop(self): """Stop processing""" self._cancel_requested = True @@ -76,6 +195,7 @@ def sync_all(self, on_progress: Callable[[SyncProgress], None] | None = None): # Get unsynced count unsynced = db.get_unsynced_screenshots() self._progress = SyncProgress(total=len(unsynced), processed=0, errors=0, is_running=True) + self._progress.current_phase = "embedding" if self._on_progress: self._on_progress(self._progress) @@ -85,11 +205,14 @@ def sync_all(self, on_progress: Callable[[SyncProgress], None] | None = None): break try: - # Generate embedding + # Phase 1: Generate CLIP embedding embedding = get_image_embedding(screenshot["image_path"]) - - # Save to database db.add_embedding(screenshot["id"], embedding) + self._progress.embeddings_done += 1 + + # Phase 2: OCR (if enabled) + if config.ocr.enabled: + self._process_ocr_for_screenshot(screenshot["id"], screenshot["image_path"]) self._progress.processed += 1 except Exception as e: @@ -107,25 +230,25 @@ def sync_all(self, on_progress: Callable[[SyncProgress], None] | None = None): def _process_loop(self, batch_size: int): """Background processing - processes all unsynced then stops""" - # Get total unsynced count upfront + # Phase 1: Process screenshots without CLIP embeddings total_unsynced = db.get_unsynced_count() + ocr_pending = db.get_ocr_pending_count() if config.ocr.enabled else 0 - if total_unsynced == 0: + total = total_unsynced + ocr_pending + if total == 0: self._running = False self._progress = SyncProgress(total=0, processed=0, errors=0, is_running=False) return - self._progress = SyncProgress(total=total_unsynced, processed=0, errors=0, is_running=True) + self._progress = SyncProgress(total=total, processed=0, errors=0, is_running=True) + self._progress.current_phase = "embedding" - print(f"🔄 Syncing {total_unsynced} screenshots...") + print(f"🔄 Syncing {total_unsynced} screenshots + {ocr_pending} OCR pending...") - # Process in batches until done + # Process CLIP embeddings in batches while self._running and not self._cancel_requested: - # Get next batch unsynced = db.get_unsynced_screenshots(limit=batch_size) - if not unsynced: - # All done! break for screenshot in unsynced: @@ -133,26 +256,247 @@ def _process_loop(self, batch_size: int): break try: - # Generate embedding + # Generate CLIP embedding embedding = get_image_embedding(screenshot["image_path"]) - - # Save to database db.add_embedding(screenshot["id"], embedding) + self._progress.embeddings_done += 1 + + # Run OCR immediately after embedding (before compression) + if config.ocr.enabled: + self._process_ocr_for_screenshot(screenshot["id"], screenshot["image_path"]) self._progress.processed += 1 print(f" ✓ {self._progress.processed}/{self._progress.total}") + except OCRProcessingError as e: + # Expected OCR failure - skip and continue + print(f" ⚠ Skipped OCR for {screenshot['id']}: {e}") + self._progress.errors += 1 + self._progress.last_error = str(e) + self._progress.error_type = "ocr_failed" + # Still count as processed since embedding worked + self._progress.processed += 1 + with contextlib.suppress(Exception): + db.mark_ocr_complete(screenshot["id"]) + except (TypeError, AttributeError, KeyError) as e: + # Likely a code bug - log clearly but continue with other screenshots + error_msg = f"{type(e).__name__}: {e}" + print(f" ❌ Code error for {screenshot['id']}: {error_msg}") + self._progress.errors += 1 + self._progress.last_error = error_msg + self._progress.error_type = "code_error" + # Don't mark as complete - can retry after fix + except (FileNotFoundError, OSError) as e: + # Image file issue - skip and continue + print(f" ⚠ Skipped {screenshot['id']}: Cannot read image - {e}") + self._progress.errors += 1 + self._progress.last_error = str(e) + self._progress.error_type = "file_error" except Exception as e: - print(f" ✗ Error: {screenshot['image_path']}: {e}") + # Other error - log and continue + error_msg = f"{type(e).__name__}: {e}" + print(f" ✗ Error: {error_msg}") self._progress.errors += 1 + self._progress.last_error = error_msg + self._progress.error_type = "unknown" if self._on_progress: self._on_progress(self._progress) + # Phase 2: Process any remaining OCR (migration for existing screenshots) + if config.ocr.enabled and not self._cancel_requested: + self._progress.current_phase = "ocr" + self._process_pending_ocr(batch_size) + # Mark as complete self._running = False self._progress.is_running = False print(f"✅ Sync complete: {self._progress.processed} processed, {self._progress.errors} errors") + def _process_ocr_for_screenshot(self, screenshot_id: int, image_path: str): + """Run OCR pipeline for a single screenshot + + Pipeline: + 1. Extract text via OCR provider + 2. Store OCR result + 3. Chunk text (small + large) + 4. Generate BGE embeddings for chunks (batched for speed) + 5. Store chunks and embeddings + + Raises: + OCRProcessingError: For expected OCR failures (corrupted image, etc.) + TypeError/AttributeError: Re-raised for code bugs (should not be silenced) + Exception: Other unexpected errors are re-raised + """ + self._progress.current_phase = "ocr" + + ocr_service = self._get_ocr_service() + text_emb_service = self._get_text_embedding_service() + chunking = self._get_chunking() + + # Step 1: Extract text + try: + ocr_result = ocr_service.extract_text(image_path) + except (FileNotFoundError, OSError) as e: + # Expected failures - image missing or corrupted + raise OCRProcessingError(f"Cannot read image: {e}") from e + except Exception: + # Unexpected OCR error - could be a bug, re-raise + raise + + # Step 2: Store OCR result (even if empty, for tracking) + # This is a critical step - if it fails, it's likely a code bug + # Returns the ocr_id which we need for adding chunks + ocr_id = db.add_ocr_text( + screenshot_id=screenshot_id, + full_text=ocr_result.text, + confidence=ocr_result.confidence, + word_count=ocr_result.word_count, + ) + + self._progress.ocr_done += 1 + + # Step 3-5: Only if we have text + if ocr_result.text.strip(): + self._progress.current_phase = "text_embedding" + + # Chunk the text (dual chunking: small + large) + chunked = chunking.chunk_ocr_text( + ocr_result.text, + small_size=config.chunking.small_size, + small_overlap=config.chunking.small_overlap, + large_size=config.chunking.large_size, + large_overlap=config.chunking.large_overlap, + ) + + # Collect all chunks for batch processing + all_chunks = [] + for chunk in chunked.small: + all_chunks.append(("small", chunk)) + for chunk in chunked.large: + all_chunks.append(("large", chunk)) + + if all_chunks: + # Batch generate embeddings (much faster than individual calls) + all_texts = [chunk.text for _, chunk in all_chunks] + all_embeddings = text_emb_service.get_batch_embeddings(all_texts) + + # Store chunks and embeddings + for (chunk_size, chunk), embedding in zip(all_chunks, all_embeddings, strict=False): + chunk_id = db.add_ocr_chunk( + ocr_id=ocr_id, + chunk_size=chunk_size, + chunk_index=chunk.index, + chunk_text=chunk.text, + start_char=chunk.start_char, + end_char=chunk.end_char, + ) + db.add_text_embedding(chunk_id, embedding) + + self._progress.text_embeddings_done += len(all_chunks) + + # Mark OCR complete for this screenshot + db.mark_ocr_complete(screenshot_id) + + def _process_pending_ocr(self, batch_size: int): + """Process OCR for screenshots that have embeddings but no OCR (migration)""" + while self._running and not self._cancel_requested: + pending = db.get_screenshots_without_ocr(limit=batch_size) + if not pending: + break + + for screenshot in pending: + if self._cancel_requested: + break + + try: + self._process_ocr_for_screenshot(screenshot["id"], screenshot["image_path"]) + self._progress.processed += 1 + print(f" ✓ {self._progress.processed}/{self._progress.total}") + except OCRProcessingError as e: + # Expected failure (corrupted image, etc.) - skip and continue + print(f" ⚠ Skipped {screenshot['id']}: {e}") + self._progress.errors += 1 + self._progress.last_error = str(e) + self._progress.error_type = "ocr_failed" + # Mark as complete so we don't retry + with contextlib.suppress(Exception): + db.mark_ocr_complete(screenshot["id"]) + except (TypeError, AttributeError, KeyError) as e: + # Likely a code bug - log clearly but continue with other screenshots + error_msg = f"{type(e).__name__}: {e}" + print(f" ❌ Code error for {screenshot['id']}: {error_msg}") + self._progress.errors += 1 + self._progress.last_error = error_msg + self._progress.error_type = "code_error" + # Don't mark as complete - can retry after fix + except Exception as e: + # Unexpected error - log but continue (could be transient) + error_msg = f"{type(e).__name__}: {e}" + print(f" ✗ Error: {error_msg}") + self._progress.errors += 1 + self._progress.last_error = error_msg + self._progress.error_type = "unknown" + + if self._on_progress: + self._on_progress(self._progress) + + def recompute_ocr(self, on_progress: Callable[[SyncProgress], None] | None = None): + """Recompute OCR for all screenshots (use after changing OCR/embedding model) + + This will: + 1. Clear all existing OCR data + 2. Re-run OCR on all screenshots + 3. Regenerate all text chunks and embeddings + """ + if self._running: + raise RuntimeError("Cannot recompute while sync is running") + + self._on_progress = on_progress + self._cancel_requested = False + + # Clear existing OCR data + print("🗑️ Clearing existing OCR data...") + db.reset_all_ocr() + + # Get total count + total = db.get_screenshot_count() + self._progress = SyncProgress(total=total, processed=0, errors=0, is_running=True) + self._progress.current_phase = "ocr" + + if self._on_progress: + self._on_progress(self._progress) + + print(f"🔄 Recomputing OCR for {total} screenshots...") + + # Process all screenshots + batch_size = 10 + while not self._cancel_requested: + screenshots = db.get_screenshots_without_ocr(limit=batch_size) + if not screenshots: + break + + for screenshot in screenshots: + if self._cancel_requested: + break + + try: + self._process_ocr_for_screenshot(screenshot["id"], screenshot["image_path"]) + self._progress.processed += 1 + print(f" ✓ {self._progress.processed}/{self._progress.total}") + except Exception as e: + print(f" ✗ Error: {e}") + self._progress.errors += 1 + + if self._on_progress: + self._on_progress(self._progress) + + self._progress.is_running = False + if self._on_progress: + self._on_progress(self._progress) + + print(f"✅ OCR recompute complete: {self._progress.processed} processed, {self._progress.errors} errors") + return self._progress + # Global processor service instance processor_service = ProcessorService() diff --git a/core/text_embeddings.py b/core/text_embeddings.py new file mode 100644 index 0000000..eec706d --- /dev/null +++ b/core/text_embeddings.py @@ -0,0 +1,392 @@ +""" +LiveRecall Text Embeddings +Specialized text embedding model for OCR semantic search + +Separate from CLIP to optimize for text-to-text similarity. +Uses lazy loading with auto-unload for memory management. + +============================================================================= +TEXT EMBEDDING MODEL OPTIONS +============================================================================= + +Current: BAAI/bge-small-en-v1.5 (384-dim, 130MB, MTEB 62.2) + - Great balance of accuracy and speed + - Optimized for semantic text similarity + +Lighter alternatives (if memory constrained): + - all-MiniLM-L6-v2: 384-dim, 80MB, MTEB 56.3 + - thenlper/gte-small: 384-dim, 70MB + +Higher accuracy alternatives: + - BAAI/bge-base-en-v1.5: 768-dim, 440MB, MTEB 63.5 + - all-mpnet-base-v2: 768-dim, 420MB + - nomic-embed-text-v1.5: 768-dim, 550MB + +To switch model: + 1. Update config: config.text_embeddings.model = "BAAI/bge-base-en-v1.5" + 2. Note: Changing dimensions requires recreating vector tables! + 3. Run: text_embedding_service.recompute_all() +============================================================================= +""" + +from __future__ import annotations + +import os +import threading +import time +import warnings + +# Suppress HuggingFace warnings +os.environ["TOKENIZERS_PARALLELISM"] = "false" +warnings.filterwarnings("ignore", message=".*use_fast.*") +warnings.filterwarnings("ignore", category=FutureWarning) + +import numpy as np # noqa: E402 +import torch # noqa: E402 + +# Lazy loading state +_model = None +_device: str | None = None +_last_used: float = 0 +_auto_unload_timer: threading.Timer | None = None +_lock = threading.Lock() + +# Configuration +AUTO_UNLOAD_SECONDS = 300 # 5 minutes of idle before unloading +DEFAULT_MODEL = "BAAI/bge-small-en-v1.5" +EMBEDDING_DIM = 384 # Dimension for bge-small + +# Model name (can be changed via config) +_current_model_name = DEFAULT_MODEL + + +def _get_device() -> str: + """Get the best available device""" + if torch.cuda.is_available(): + return "cuda" + elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): + return "mps" # Apple Silicon + return "cpu" + + +def _load_model(model_name: str | None = None): + """Load the text embedding model (only called once)""" + global _model, _device, _last_used, _current_model_name + + if model_name is None: + model_name = _current_model_name + + with _lock: + if _model is not None: + _last_used = time.time() + return _model + + print(f"Loading text embedding model: {model_name}...") + + from sentence_transformers import SentenceTransformer + + _device = _get_device() + print(f"Using device: {_device}") + + try: + _model = SentenceTransformer(model_name, device=_device) + _current_model_name = model_name + except Exception as e: + print(f"Error loading on {_device}, falling back to CPU: {e}") + _device = "cpu" + _model = SentenceTransformer(model_name, device="cpu") + _current_model_name = model_name + + _last_used = time.time() + print(f"Text embedding model loaded successfully (dim={_model.get_sentence_embedding_dimension()})") + + # Start auto-unload timer + _schedule_auto_unload() + + return _model + + +def _schedule_auto_unload(): + """Schedule auto-unload after idle timeout""" + global _auto_unload_timer + + # Cancel existing timer + if _auto_unload_timer is not None: + _auto_unload_timer.cancel() + + if AUTO_UNLOAD_SECONDS > 0: + # Schedule new timer + _auto_unload_timer = threading.Timer(AUTO_UNLOAD_SECONDS, _check_and_unload) + _auto_unload_timer.daemon = True + _auto_unload_timer.start() + + +def _check_and_unload(): + """Check if model should be unloaded due to inactivity""" + global _last_used + + idle_time = time.time() - _last_used + if idle_time >= AUTO_UNLOAD_SECONDS: + print(f"Text embedding model idle for {idle_time:.0f}s, unloading...") + unload_model() + else: + # Reschedule check + _schedule_auto_unload() + + +def unload_model(): + """Explicitly unload the text embedding model to free memory""" + global _model, _device, _auto_unload_timer + + with _lock: + if _model is None: + return + + print("Unloading text embedding model...") + + # Cancel auto-unload timer + if _auto_unload_timer is not None: + _auto_unload_timer.cancel() + _auto_unload_timer = None + + # Delete model + del _model + _model = None + + # Clear GPU memory if applicable + if _device == "cuda": + torch.cuda.empty_cache() + + _device = None + print("Text embedding model unloaded") + + +def is_loaded() -> bool: + """Check if the model is currently loaded""" + return _model is not None + + +def is_downloaded() -> bool: + """Check if the text embedding model is downloaded in HuggingFace cache""" + try: + from huggingface_hub import try_to_load_from_cache + + # Check for the sentence-transformers config file + result = try_to_load_from_cache(_current_model_name, "config_sentence_transformers.json") + if result is not None: + return True + # Fallback: some models use config.json + result = try_to_load_from_cache(_current_model_name, "config.json") + return result is not None + except Exception: + return False + + +def get_model_status() -> dict: + """Get detailed model status""" + return { + "loaded": _model is not None, + "downloaded": is_downloaded(), + "model_name": _current_model_name, + "device": _device, + "last_used": _last_used, + "idle_seconds": time.time() - _last_used if _last_used > 0 else 0, + "auto_unload_seconds": AUTO_UNLOAD_SECONDS, + "embedding_dim": EMBEDDING_DIM, + } + + +def set_auto_unload_timeout(seconds: int): + """Set the auto-unload timeout (0 to disable)""" + global AUTO_UNLOAD_SECONDS + AUTO_UNLOAD_SECONDS = seconds + if seconds > 0 and _model is not None: + _schedule_auto_unload() + + +def _normalize(embedding: np.ndarray) -> list[float]: + """Normalize embedding to unit length for cosine similarity""" + arr = np.array(embedding) + norm = np.linalg.norm(arr) + if norm > 0: + arr = arr / norm + return arr.tolist() + + +def get_text_embedding(text: str) -> list[float]: + """ + Generate normalized embedding for a text string. + + Args: + text: The text to encode + + Returns: + Normalized embedding as list of floats (384-dim for bge-small) + """ + global _last_used + + model = _load_model() + _last_used = time.time() + + try: + # BGE models work best with instruction prefix for queries + # But for storing document chunks, we use text as-is + embedding = model.encode(text, convert_to_tensor=False) + return _normalize(embedding) + except Exception as e: + print(f"Error generating text embedding: {e}") + raise + + +def get_query_embedding(query: str) -> list[float]: + """ + Generate embedding for a search query. + + For BGE models, queries should be prefixed with "Represent this sentence:" + for optimal retrieval performance. + + Args: + query: The search query text + + Returns: + Normalized embedding as list of floats + """ + global _last_used + + model = _load_model() + _last_used = time.time() + + try: + # BGE instruction for query encoding + instruction = "Represent this sentence for searching relevant passages: " + embedding = model.encode(instruction + query, convert_to_tensor=False) + return _normalize(embedding) + except Exception as e: + print(f"Error generating query embedding: {e}") + raise + + +def get_batch_embeddings(texts: list[str], show_progress: bool = False) -> list[list[float]]: + """ + Generate embeddings for multiple texts efficiently. + + Args: + texts: List of texts to encode + show_progress: Whether to show progress bar + + Returns: + List of normalized embeddings + """ + global _last_used + + if not texts: + return [] + + model = _load_model() + _last_used = time.time() + + try: + embeddings = model.encode(texts, convert_to_tensor=False, batch_size=32, show_progress_bar=show_progress) + + # Normalize all embeddings + result = [] + for emb in embeddings: + result.append(_normalize(emb)) + + return result + except Exception as e: + print(f"Error generating batch embeddings: {e}") + raise + + +def get_embedding_dimension() -> int: + """Get the dimension of embeddings produced by the current model""" + if _model is not None: + return _model.get_sentence_embedding_dimension() + return EMBEDDING_DIM + + +def cosine_similarity(emb1: list[float], emb2: list[float]) -> float: + """Calculate cosine similarity between two embeddings""" + a = np.array(emb1) + b = np.array(emb2) + norm_a = np.linalg.norm(a) + norm_b = np.linalg.norm(b) + if norm_a == 0 or norm_b == 0: + return 0.0 + return float(np.dot(a, b) / (norm_a * norm_b)) + + +# ============================================================================= +# Service wrapper for consistent interface with other services +# ============================================================================= + + +class TextEmbeddingService: + """Wrapper class for text embedding functions to provide service-like interface. + + This wrapper provides the same functionality as the module functions + but in a class-based API for consistency with other services. + """ + + def get_text_embedding(self, text: str) -> list[float]: + """Generate embedding for document text""" + return get_text_embedding(text) + + def get_query_embedding(self, query: str) -> list[float]: + """Generate embedding for search query""" + return get_query_embedding(query) + + def get_batch_embeddings(self, texts: list[str], show_progress: bool = False) -> list[list[float]]: + """Generate embeddings for multiple texts""" + return get_batch_embeddings(texts, show_progress) + + def get_model_status(self) -> dict: + """Get model status""" + return get_model_status() + + def unload_model(self): + """Unload the model""" + unload_model() + + def is_loaded(self) -> bool: + """Check if model is loaded""" + return is_loaded() + + def is_downloaded(self) -> bool: + """Check if model is downloaded""" + return is_downloaded() + + +# Global service instance +text_embedding_service = TextEmbeddingService() + + +if __name__ == "__main__": + # Test text embeddings module + print("Testing text embeddings module...") + print(f"Status: {get_model_status()}") + + # Test text embedding (this will load the model) + text_emb = get_text_embedding("This is a test sentence for embedding") + print(f"Text embedding shape: {len(text_emb)}") + print(f"Status after load: {get_model_status()}") + + # Test query embedding + query_emb = get_query_embedding("search for test") + print(f"Query embedding shape: {len(query_emb)}") + + # Test batch embeddings + batch_emb = get_batch_embeddings(["Hello world", "How are you?", "Test batch"]) + print(f"Batch embeddings: {len(batch_emb)} embeddings") + + # Test similarity + sim = cosine_similarity(text_emb, query_emb) + print(f"Similarity between text and query: {sim:.4f}") + + # Test unload + print("Testing unload...") + unload_model() + print(f"Status after unload: {get_model_status()}") + + print("Done!") diff --git a/main.py b/main.py index 372807a..74f0a4c 100644 --- a/main.py +++ b/main.py @@ -56,6 +56,7 @@ def main(): host=args.host, port=args.port, reload=args.reload if not is_frozen() else False, + log_level="warning", # Suppress verbose access logs ) else: # Launch system tray (default) diff --git a/pyproject.toml b/pyproject.toml index e76afb3..8c21f55 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,12 @@ dependencies = [ "torch==2.3.0", "transformers>=4.55.2", + # OCR (lazy loaded, platform-specific) + # Apple Vision via ocrmac (macOS only, uses native Vision framework) + "ocrmac>=1.0.0; sys_platform == 'darwin'", + # Tesseract OCR (cross-platform fallback) + "pytesseract>=0.3.10", + # API "fastapi>=0.115.0", "uvicorn>=0.32.0", diff --git a/scripts/benchmark_ocr.py b/scripts/benchmark_ocr.py new file mode 100644 index 0000000..2650db9 --- /dev/null +++ b/scripts/benchmark_ocr.py @@ -0,0 +1,446 @@ +#!/usr/bin/env python3 +""" +Benchmark script for OCR and embedding performance. + +Tests: +1. CLIP image embedding +2. OCR text extraction +3. Text chunking +4. Text embedding generation (single + batch) + +Usage: + uv run python scripts/benchmark_ocr.py + uv run python scripts/benchmark_ocr.py --count 50 + uv run python scripts/benchmark_ocr.py --show-ocr +""" + +import argparse +import contextlib +import os +import platform +import random +import statistics +import sys +import time +from pathlib import Path + +# Add project root to path +sys.path.insert(0, str(Path(__file__).parent.parent)) + + +def warmup_models(): + """Pre-load all models so benchmark isn't skewed by loading time""" + print("\n" + "=" * 60) + print("WARMING UP MODELS (loading into memory)") + print("=" * 60) + + # CLIP model + print(" Loading CLIP model...", end=" ", flush=True) + start = time.perf_counter() + # Create a tiny test image to trigger model load + import tempfile + + from PIL import Image + + from core.embeddings import get_image_embedding + + with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as f: + img = Image.new("RGB", (100, 100), color="white") + img.save(f.name) + with contextlib.suppress(Exception): + get_image_embedding(f.name) + Path(f.name).unlink() + print(f"done ({time.perf_counter() - start:.1f}s)") + + # OCR service + print(" Loading OCR service...", end=" ", flush=True) + start = time.perf_counter() + from core.ocr import ocr_service + + if ocr_service.is_available(): + print(f"done - {ocr_service.get_provider_name()} ({time.perf_counter() - start:.1f}s)") + else: + print("not available") + + # Text embedding model + print(" Loading text embedding model...", end=" ", flush=True) + start = time.perf_counter() + from core.text_embeddings import text_embedding_service + + text_embedding_service.get_text_embedding("warmup test") + print(f"done ({time.perf_counter() - start:.1f}s)") + + print(" All models loaded!\n") + + +def get_random_images(directory: str, count: int) -> list[Path]: + """Get random sample of images from directory""" + dir_path = Path(directory) + if not dir_path.exists(): + print(f"Error: Directory not found: {directory}") + sys.exit(1) + + images = list(dir_path.glob("*.jpg")) + list(dir_path.glob("*.jpeg")) + list(dir_path.glob("*.png")) + if not images: + print(f"Error: No images found in {directory}") + sys.exit(1) + + count = min(count, len(images)) + return random.sample(images, count) + + +def benchmark_clip(images: list[Path]) -> dict: + """Benchmark CLIP image embedding""" + from core.embeddings import get_image_embedding + + print("\n" + "=" * 60) + print("CLIP IMAGE EMBEDDING BENCHMARK") + print("=" * 60) + + times = [] + for i, img in enumerate(images): + start = time.perf_counter() + embedding = get_image_embedding(str(img)) + elapsed = time.perf_counter() - start + times.append(elapsed) + + if i < 3: # Show first few + print(f" [{i + 1}] {img.name}: {elapsed * 1000:.1f}ms (dim={len(embedding)})") + elif i == 3: + print(f" ... processing {len(images) - 3} more ...") + + return { + "name": "CLIP Embedding", + "count": len(times), + "total": sum(times), + "mean": statistics.mean(times), + "median": statistics.median(times), + "stdev": statistics.stdev(times) if len(times) > 1 else 0, + "min": min(times), + "max": max(times), + } + + +def benchmark_ocr(images: list[Path], show_output: bool = False) -> dict: + """Benchmark OCR text extraction""" + from core.ocr import ocr_service + + print("\n" + "=" * 60) + print("OCR TEXT EXTRACTION BENCHMARK") + print("=" * 60) + + if not ocr_service.is_available(): + print(" ERROR: OCR service not available") + return {"name": "OCR", "error": "not available"} + + print(f" Provider: {ocr_service.get_provider_name()}") + + times = [] + results = [] + for i, img in enumerate(images): + start = time.perf_counter() + result = ocr_service.extract_text(str(img)) + elapsed = time.perf_counter() - start + times.append(elapsed) + results.append(result) + + word_count = result.word_count + text_preview = ( + result.text[:50].replace("\n", " ") + "..." if len(result.text) > 50 else result.text.replace("\n", " ") + ) + + if i < 5 or show_output: # Show first few or all if requested + print(f" [{i + 1}] {img.name}: {elapsed * 1000:.1f}ms") + conf_str = f"{result.confidence:.2f}" if result.confidence is not None else "N/A" + print(f" Words: {word_count}, Confidence: {conf_str}") + if show_output: + print(f" Text: {text_preview}") + print() + elif i == 5: + print(f" ... processing {len(images) - 5} more ...") + + # Show OCR result structure + if results: + print("\n OCR Result Structure:") + print(" - text: str (full extracted text)") + print(" - confidence: float | None (0-1)") + print(" - word_count: int") + print(" - language: str (default 'en')") + + # Show a full example + if show_output and results[0].text: + print("\n Full OCR Output Example (first image with text):") + for r in results: + if r.text.strip(): + print("-" * 40) + print(r.text[:500]) + if len(r.text) > 500: + print(f"... ({len(r.text)} chars total)") + print("-" * 40) + break + + return { + "name": "OCR Extraction", + "count": len(times), + "total": sum(times), + "mean": statistics.mean(times), + "median": statistics.median(times), + "stdev": statistics.stdev(times) if len(times) > 1 else 0, + "min": min(times), + "max": max(times), + "avg_words": statistics.mean([r.word_count for r in results]), + "empty_count": sum(1 for r in results if not r.text.strip()), + } + + +def benchmark_chunking(ocr_texts: list[str]) -> dict: + """Benchmark text chunking""" + from core import chunking + + print("\n" + "=" * 60) + print("TEXT CHUNKING BENCHMARK") + print("=" * 60) + + times = [] + chunk_counts = [] + + for i, text in enumerate(ocr_texts): + if not text.strip(): + continue + + start = time.perf_counter() + result = chunking.chunk_ocr_text( + text, + small_size=512, + small_overlap=50, + large_size=2048, + large_overlap=200, + ) + elapsed = time.perf_counter() - start + times.append(elapsed) + + total_chunks = len(result.small) + len(result.large) + chunk_counts.append(total_chunks) + + if i < 3: + print(f" [{i + 1}] {len(text)} chars: {elapsed * 1000:.2f}ms") + print(f" Small chunks: {len(result.small)}, Large chunks: {len(result.large)}") + + if not times: + print(" No text to chunk (all OCR results were empty)") + return {"name": "Chunking", "error": "no text"} + + print("\n Chunk Structure (ChunkInfo):") + print(" - text: str (chunk content)") + print(" - start_char: int") + print(" - end_char: int") + print(" - index: int") + + return { + "name": "Text Chunking", + "count": len(times), + "total": sum(times), + "mean": statistics.mean(times), + "median": statistics.median(times), + "stdev": statistics.stdev(times) if len(times) > 1 else 0, + "min": min(times), + "max": max(times), + "avg_chunks": statistics.mean(chunk_counts), + } + + +def benchmark_text_embedding(texts: list[str]) -> dict: + """Benchmark text embedding generation""" + from core.text_embeddings import text_embedding_service + + print("\n" + "=" * 60) + print("TEXT EMBEDDING BENCHMARK") + print("=" * 60) + + # Filter to non-empty texts and take a sample + valid_texts = [t[:1000] for t in texts if t.strip()][:50] # Cap at 1000 chars, 50 texts + + if not valid_texts: + print(" No valid texts to embed") + return {"name": "Text Embedding", "error": "no text"} + + print( + f" Model: {text_embedding_service._current_model_name if hasattr(text_embedding_service, '_current_model_name') else 'BGE'}" + ) + + # Single embedding benchmark + print("\n Single embedding (one at a time):") + single_times = [] + for i, text in enumerate(valid_texts[:10]): # Just first 10 for single + start = time.perf_counter() + emb = text_embedding_service.get_text_embedding(text) + elapsed = time.perf_counter() - start + single_times.append(elapsed) + if i < 3: + print(f" [{i + 1}] {len(text)} chars: {elapsed * 1000:.1f}ms (dim={len(emb)})") + + # Batch embedding benchmark + print("\n Batch embedding (all at once):") + batch_sizes = [10, 25, 50] + batch_results = {} + + for batch_size in batch_sizes: + if batch_size > len(valid_texts): + continue + + batch = valid_texts[:batch_size] + start = time.perf_counter() + _ = text_embedding_service.get_batch_embeddings(batch) + elapsed = time.perf_counter() - start + + per_item = elapsed / batch_size + batch_results[batch_size] = { + "total": elapsed, + "per_item": per_item, + } + print(f" Batch of {batch_size}: {elapsed * 1000:.1f}ms total, {per_item * 1000:.1f}ms/item") + + return { + "name": "Text Embedding", + "single_mean": statistics.mean(single_times), + "single_count": len(single_times), + "batch_results": batch_results, + } + + +def print_summary(results: list[dict]): + """Print summary of all benchmarks""" + print("\n" + "=" * 60) + print("SUMMARY") + print("=" * 60) + + for r in results: + if "error" in r: + print(f"\n {r['name']}: ERROR - {r['error']}") + continue + + print(f"\n {r['name']}:") + if "mean" in r: + print(f" Mean: {r['mean'] * 1000:.1f}ms") + print(f" Median: {r['median'] * 1000:.1f}ms") + print(f" Min: {r['min'] * 1000:.1f}ms") + print(f" Max: {r['max'] * 1000:.1f}ms") + if r.get("stdev"): + print(f" StdDev: {r['stdev'] * 1000:.1f}ms") + if "avg_words" in r: + print(f" Avg words/image: {r['avg_words']:.1f}") + print(f" Empty OCR count: {r['empty_count']}") + if "avg_chunks" in r: + print(f" Avg chunks/text: {r['avg_chunks']:.1f}") + if "batch_results" in r: + print(f" Single embedding: {r['single_mean'] * 1000:.1f}ms") + for bs, br in r["batch_results"].items(): + print(f" Batch {bs}: {br['per_item'] * 1000:.1f}ms/item ({br['total'] * 1000:.0f}ms total)") + + # Estimate total processing time per image + print("\n" + "-" * 60) + print(" ESTIMATED TOTAL PER IMAGE:") + + clip_time = next((r["mean"] for r in results if r["name"] == "CLIP Embedding" and "mean" in r), 0) + ocr_time = next((r["mean"] for r in results if r["name"] == "OCR Extraction" and "mean" in r), 0) + chunk_time = next((r["mean"] for r in results if r["name"] == "Text Chunking" and "mean" in r), 0) + + # Get best batch embedding time + emb_result = next((r for r in results if r["name"] == "Text Embedding"), None) + emb_time = 0 + avg_chunks = next((r["avg_chunks"] for r in results if "avg_chunks" in r), 6) + if emb_result and "batch_results" in emb_result: + # Use largest batch per-item time + largest = max(emb_result["batch_results"].keys()) + emb_time = emb_result["batch_results"][largest]["per_item"] + emb_time = emb_time * avg_chunks # Multiply by avg chunks per image + + total = clip_time + ocr_time + chunk_time + emb_time + print(f" CLIP: {clip_time * 1000:.0f}ms") + print(f" OCR: {ocr_time * 1000:.0f}ms") + print(f" Chunking: {chunk_time * 1000:.0f}ms") + if emb_time > 0: + print(f" Embedding: {emb_time * 1000:.0f}ms (batch, ~{avg_chunks:.0f} chunks)") + else: + print(" Embedding: skipped") + print(" ─────────────────────") + print(f" TOTAL: {total * 1000:.0f}ms/image") + print(f"\n For 20,000 images: ~{total * 20000 / 60:.0f} minutes") + + +def get_default_screenshots_dir() -> str: + """Get platform-appropriate default screenshots directory""" + system = platform.system() + if system == "Darwin": + return str(Path.home() / "Library/Application Support/LiveRecall/screenshots") + elif system == "Windows": + appdata = os.environ.get("APPDATA", str(Path.home() / "AppData/Roaming")) + return str(Path(appdata) / "LiveRecall/screenshots") + else: + return str(Path.home() / ".local/share/LiveRecall/screenshots") + + +def main(): + parser = argparse.ArgumentParser(description="Benchmark OCR and embedding performance") + parser.add_argument( + "--dir", + default=None, + help="Directory containing screenshots (default: platform-specific LiveRecall data dir)", + ) + parser.add_argument("--count", type=int, default=100, help="Number of images to test") + parser.add_argument("--show-ocr", action="store_true", help="Show full OCR output for each image") + parser.add_argument("--skip-clip", action="store_true", help="Skip CLIP benchmark") + parser.add_argument("--skip-ocr", action="store_true", help="Skip OCR benchmark") + parser.add_argument("--skip-embedding", action="store_true", help="Skip embedding benchmark") + + args = parser.parse_args() + + # Use platform-appropriate default if not specified + if args.dir is None: + args.dir = get_default_screenshots_dir() + + print(f"Benchmarking with {args.count} random images from:") + print(f" {args.dir}") + + images = get_random_images(args.dir, args.count) + print(f" Found {len(images)} images") + + # Warmup - load all models first + warmup_models() + + results = [] + + # CLIP benchmark + if not args.skip_clip: + results.append(benchmark_clip(images[:20])) # CLIP is slow, just do 20 + + # OCR benchmark + ocr_texts = [] + if not args.skip_ocr: + ocr_result = benchmark_ocr(images, show_output=args.show_ocr) + results.append(ocr_result) + + # Get OCR texts for chunking benchmark + from core.ocr import ocr_service + + for img in images: + try: + r = ocr_service.extract_text(str(img)) + ocr_texts.append(r.text) + except Exception: + ocr_texts.append("") + + # Chunking benchmark + if ocr_texts: + results.append(benchmark_chunking(ocr_texts)) + + # Text embedding benchmark + if not args.skip_embedding and ocr_texts: + results.append(benchmark_text_embedding(ocr_texts)) + + # Summary + print_summary(results) + + +if __name__ == "__main__": + main() diff --git a/scripts/cleanup_ocr.py b/scripts/cleanup_ocr.py new file mode 100644 index 0000000..cd8fbed --- /dev/null +++ b/scripts/cleanup_ocr.py @@ -0,0 +1,109 @@ +#!/usr/bin/env python3 +""" +Cleanup script to reset OCR data before restarting the app. +Run this after fixing bugs in the OCR processing pipeline. + +Usage: + uv run python scripts/cleanup_ocr.py +""" + +import sys +from pathlib import Path + +# Add project root to path +project_root = Path(__file__).parent.parent +sys.path.insert(0, str(project_root)) + +from core.database import db # noqa: E402 + + +def main() -> None: + print("OCR Data Cleanup Script") + print("=" * 50) + + # Connect to database + db.connect() + + try: + # Get current stats + with db.cursor() as cur: + cur.execute("SELECT COUNT(*) FROM screenshots") + total_screenshots = cur.fetchone()[0] + + cur.execute("SELECT COUNT(*) FROM screenshots WHERE has_ocr = 1") + with_ocr = cur.fetchone()[0] + + cur.execute("SELECT COUNT(*) FROM screenshot_ocr") + ocr_records = cur.fetchone()[0] + + cur.execute("SELECT COUNT(*) FROM ocr_text_chunks") + chunk_records = cur.fetchone()[0] + + cur.execute("SELECT COUNT(*) FROM ocr_text_embeddings") + embedding_records = cur.fetchone()[0] + + print("\nCurrent database state:") + print(f" Total screenshots: {total_screenshots}") + print(f" Screenshots with has_ocr=1: {with_ocr}") + print(f" OCR records: {ocr_records}") + print(f" Chunk records: {chunk_records}") + print(f" Text embedding records: {embedding_records}") + + # Check for inconsistencies + with db.cursor() as cur: + # Screenshots marked as having OCR but no actual OCR record + cur.execute(""" + SELECT COUNT(*) FROM screenshots s + WHERE s.has_ocr = 1 + AND NOT EXISTS (SELECT 1 FROM screenshot_ocr o WHERE o.screenshot_id = s.id) + """) + orphan_has_ocr = cur.fetchone()[0] + + # OCR records without any chunks (could be intentional if text was empty) + cur.execute(""" + SELECT COUNT(*) FROM screenshot_ocr o + WHERE NOT EXISTS (SELECT 1 FROM ocr_text_chunks c WHERE c.ocr_id = o.id) + AND o.full_text != '' + """) + ocr_without_chunks = cur.fetchone()[0] + + # Chunks without embeddings + cur.execute(""" + SELECT COUNT(*) FROM ocr_text_chunks c + WHERE NOT EXISTS (SELECT 1 FROM ocr_text_embeddings e WHERE e.chunk_id = c.id) + """) + chunks_without_embeddings = cur.fetchone()[0] + + print("\nInconsistencies found:") + print(f" Screenshots with has_ocr=1 but no OCR record: {orphan_has_ocr}") + print(f" OCR records with text but no chunks: {ocr_without_chunks}") + print(f" Chunks without embeddings: {chunks_without_embeddings}") + + if orphan_has_ocr > 0 or ocr_without_chunks > 0 or chunks_without_embeddings > 0: + print("\n" + "=" * 50) + print("Found inconsistencies! Recommend full OCR reset.") + print("=" * 50) + + response = input("\nReset ALL OCR data and reprocess? (y/N): ") + if response.lower() == "y": + print("\nResetting all OCR data...") + count = db.reset_all_ocr() + print(f"Reset complete. {count} screenshots will be reprocessed on next sync.") + else: + print("\nSkipped reset. You can manually fix issues or run this script again.") + else: + print("\nNo inconsistencies found!") + response = input("\nDo you still want to reset ALL OCR data? (y/N): ") + if response.lower() == "y": + print("\nResetting all OCR data...") + count = db.reset_all_ocr() + print(f"Reset complete. {count} screenshots will be reprocessed on next sync.") + + finally: + db.disconnect() + + print("\nDone! You can now restart the app.") + + +if __name__ == "__main__": + main() diff --git a/tray/app.py b/tray/app.py index 483b0fe..22902ac 100644 --- a/tray/app.py +++ b/tray/app.py @@ -113,13 +113,35 @@ def _check_and_open_setup(self): # Wait a moment for backend to be ready time.sleep(1) - # Check setup status via API - status = api_client.get_json("/setup/status") + # Check enhanced setup status via API (includes model and migration status) + status = api_client.get_json("/setup/enhanced-status") - if status and status.get("needs_setup", False): + if not status: + return + + should_open_setup = False + reason = "" + + # Check for version change + if status.get("needs_setup", False): last_version = status.get("last_seen_version", "none") current_version = status.get("current_version", "unknown") - print(f"Version change detected: {last_version} -> {current_version}") + reason = f"Version change detected: {last_version} -> {current_version}" + should_open_setup = True + + # Check if CLIP model needs downloading (required for search) + elif status.get("clip_status") == "not_downloaded": + reason = "CLIP model needs downloading" + should_open_setup = True + + # Check if OCR migration is needed + elif status.get("migration_status") and status["migration_status"].get("needs_migration", False): + pending = status["migration_status"].get("screenshots_without_ocr", 0) + reason = f"OCR migration needed for {pending} screenshots" + should_open_setup = True + + if should_open_setup: + print(f"{reason}") print("Opening setup page...") webbrowser.open(f"{WEB_UI_URL}/setup") except Exception as e: diff --git a/uv.lock b/uv.lock index b3fba8f..e582814 100644 --- a/uv.lock +++ b/uv.lock @@ -589,11 +589,13 @@ dependencies = [ { name = "httpx" }, { name = "mss" }, { name = "numpy" }, + { name = "ocrmac", marker = "sys_platform == 'darwin'" }, { name = "opencv-python" }, { name = "opencv-python-headless" }, { name = "pillow" }, { name = "pydantic" }, { name = "pystray" }, + { name = "pytesseract" }, { name = "requests" }, { name = "scikit-image" }, { name = "sentence-transformers" }, @@ -633,12 +635,14 @@ requires-dist = [ { name = "httpx", marker = "extra == 'dev'", specifier = ">=0.27.0" }, { name = "mss", specifier = ">=9.0.0" }, { name = "numpy", specifier = "==1.26.0" }, + { name = "ocrmac", marker = "sys_platform == 'darwin'", specifier = ">=1.0.0" }, { name = "opencv-python", specifier = "==4.9.0.80" }, { name = "opencv-python-headless", specifier = "==4.9.0.80" }, { name = "pillow", specifier = "==10.3.0" }, { name = "pydantic", specifier = ">=2.0.0" }, { name = "pyinstaller", marker = "extra == 'build'", specifier = ">=6.0.0" }, { name = "pystray", specifier = ">=0.19.0" }, + { name = "pytesseract", specifier = ">=0.3.10" }, { name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" }, { name = "pytest-cov", marker = "extra == 'dev'", specifier = ">=4.0.0" }, { name = "requests", specifier = ">=2.32.4" }, @@ -1023,6 +1027,20 @@ wheels = [ { url = 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'visible_only', diff --git a/web/src/app/page.tsx b/web/src/app/page.tsx index e22d687..ee6949f 100644 --- a/web/src/app/page.tsx +++ b/web/src/app/page.tsx @@ -19,8 +19,10 @@ import { bulkDeleteScreenshots, bulkHideScreenshots, bulkUnhideScreenshots, + getScreenshotOCR, + type ScreenshotOCR, } from '@/lib/api'; -import type { Screenshot, SystemStatus, SyncStatus, DensityBucket, IncognitoStatus, VisibilityFilter } from '@/types'; +import type { Screenshot, SystemStatus, SyncStatus, DensityBucket, IncognitoStatus, VisibilityFilter, SearchMode } from '@/types'; import { useSelection } from '@/hooks/useSelection'; import { SelectionToolbar } from '@/components/SelectionToolbar'; import { ConfirmationDialog } from '@/components/ConfirmationDialog'; @@ -59,9 +61,13 @@ export default function Home() { const [searchStartDate, setSearchStartDate] = useState(); const [searchEndDate, setSearchEndDate] = useState(); const [selectedImage, setSelectedImage] = useState(null); + const [showOCRModal, setShowOCRModal] = useState(false); + const [ocrData, setOcrData] = useState(null); + const [isLoadingOCR, setIsLoadingOCR] = useState(false); const [datePreset, setDatePreset] = useState('all'); const [safeMode, setSafeMode] = useState(true); const [safeModeLevel, setSafeModeLevel] = useState('mid'); + const [searchMode, setSearchMode] = useState('auto'); const [hasTriggeredSync, setHasTriggeredSync] = useState(false); // Incognito state @@ -428,7 +434,7 @@ export default function Home() { setIsSearching(true); try { const startTime = performance.now(); - const data = await search(query, 50, safeMode, safeModeLevel, searchStartDate, searchEndDate, visibilityFilter); + const data = await search(query, 50, safeMode, safeModeLevel, searchStartDate, searchEndDate, visibilityFilter, searchMode); setSearchResults(data.results); setSearchTime(performance.now() - startTime); } catch (err) { @@ -440,7 +446,7 @@ export default function Home() { }, 300); return () => clearTimeout(timer); - }, [query, searchStartDate, searchEndDate, safeMode, safeModeLevel, visibilityFilter]); + }, [query, searchStartDate, searchEndDate, safeMode, safeModeLevel, visibilityFilter, searchMode]); useEffect(() => { const handleKeyDown = (e: KeyboardEvent) => { @@ -753,7 +759,7 @@ export default function Home() { }); } else if (activeView === 'search') { if (query.trim()) { - const data = await search(query, 50, safeMode, safeModeLevel, searchStartDate, searchEndDate, visibilityFilter); + const data = await search(query, 50, safeMode, safeModeLevel, searchStartDate, searchEndDate, visibilityFilter, searchMode); setSearchResults(data?.results || []); } else { const data = await getScreenshots(100, 0, undefined, undefined, visibilityFilter); @@ -920,6 +926,29 @@ export default function Home() { } }, [visibilityFilter, loadWindowAtOffset]); + // View OCR text for a screenshot + const viewOCRText = useCallback(async (screenshot: Screenshot) => { + setIsLoadingOCR(true); + setOcrData(null); + setShowOCRModal(true); + try { + const data = await getScreenshotOCR(screenshot.id); + setOcrData(data); + } catch (err) { + console.error('Failed to fetch OCR text:', err); + setOcrData({ has_ocr: false, text: '', confidence: null, word_count: 0 }); + } finally { + setIsLoadingOCR(false); + } + }, []); + + // Copy OCR text to clipboard + const copyOCRText = useCallback(() => { + if (ocrData?.text) { + navigator.clipboard.writeText(ocrData.text); + } + }, [ocrData]); + // Calculate local index within the loaded window const localIndex = currentIndex - windowOffset; const currentSnapshot = (snapshots.length > 0 && localIndex >= 0 && localIndex < snapshots.length) @@ -1270,6 +1299,18 @@ export default function Home() { )} + {/* Search mode selector */} + {/* Results info */} @@ -1495,6 +1536,95 @@ export default function Home() { View in Grid + + + + )} + + {/* OCR Text Modal */} + {showOCRModal && ( +
setShowOCRModal(false)} + > +
e.stopPropagation()} + > + {/* Header */} +
+

Extracted Text

+ +
+ + {/* Content */} +
+ {isLoadingOCR ? ( +
+
+ Loading... +
+ ) : ocrData?.has_ocr === false ? ( +
+ + + +

OCR not yet processed for this screenshot

+
+ ) : ocrData?.text ? ( + 
+                  {ocrData.text}
+
+ ) : ( +
+ + + +

No text found in this screenshot

+
+ )} +
+ + {/* Footer */} + {ocrData?.has_ocr && ( +
+
+ {ocrData.word_count} words + {ocrData.confidence !== null && ( + {(ocrData.confidence * 100).toFixed(0)}% confidence + )} +
+ {ocrData.text && ( + + )} +
+ )}
)} diff --git a/web/src/app/setup/page.tsx b/web/src/app/setup/page.tsx index d51dcfc..db04d6e 100644 --- a/web/src/app/setup/page.tsx +++ b/web/src/app/setup/page.tsx @@ -1,68 +1,212 @@ 'use client'; -import { useState, useEffect } from 'react'; +import { useState, useEffect, useCallback } from 'react'; import Link from 'next/link'; +// ============================================================================= +// Types +// ============================================================================= + interface SetupStatus { current_version: string; last_seen_version: string; needs_setup: boolean; + needs_permission: boolean; + platform: string; +} + +interface EnhancedSetupStatus extends SetupStatus { + models_ready: boolean; + clip_status: 'not_downloaded' | 'downloading' | 'ready'; + text_embedding_status: 'not_downloaded' | 'downloading' | 'ready'; + ocr_status: 'ready' | 'not_available'; + migration_status: MigrationStatus | null; +} + +interface MigrationStatus { + needs_migration: boolean; + total_screenshots: number; + screenshots_with_ocr: number; + screenshots_without_ocr: number; + progress_percent: number; + estimated_time_minutes: number | null; +} + +interface SyncProgress { + is_syncing: boolean; + total: number; + processed: number; + current_phase: string; + embeddings_done: number; + ocr_done: number; + text_embeddings_done: number; +} + +interface EventStatus { + clip: { loaded: boolean; downloading: boolean; downloaded: boolean }; + text_embedding: { loaded: boolean; downloading: boolean; downloaded: boolean }; + ocr: { available: boolean }; + sync: SyncProgress; + ocr_stats: { pending: number; completed: number }; } -type SetupStep = 'welcome' | 'resetting' | 'granting' | 'complete'; +type SetupStep = 'loading' | 'models' | 'permission' | 'migration' | 'complete'; + +// ============================================================================= +// Component +// ============================================================================= export default function SetupPage() { - const [status, setStatus] = useState(null); - const [step, setStep] = useState('welcome'); + const [status, setStatus] = useState(null); + const [eventStatus, setEventStatus] = useState(null); + const [step, setStep] = useState('loading'); const [error, setError] = useState(null); - const [autoResetTriggered, setAutoResetTriggered] = useState(false); + const [syncTriggered, setSyncTriggered] = useState(false); + const [downloadTriggered, setDownloadTriggered] = useState(false); - useEffect(() => { - fetchStatus(); + // Fetch enhanced setup status + const fetchStatus = useCallback(async () => { + try { + const response = await fetch('/api/v1/setup/enhanced-status'); + const data: EnhancedSetupStatus = await response.json(); + setStatus(data); + return data; + } catch (err) { + console.error('Failed to fetch setup status:', err); + return null; + } }, []); - // Auto-trigger reset when page loads and setup is needed - useEffect(() => { - if (status?.needs_setup && !autoResetTriggered && step === 'welcome') { - setAutoResetTriggered(true); - handleResetPermissions(); + // Fetch real-time status from events endpoint + const fetchEventStatus = useCallback(async () => { + try { + const response = await fetch('/api/v1/events/status'); + const data: EventStatus = await response.json(); + setEventStatus(data); + return data; + } catch (err) { + console.error('Failed to fetch event status:', err); + return null; } - }, [status, autoResetTriggered, step]); + }, []); - const fetchStatus = async () => { - try { - const response = await fetch('/api/v1/setup/status'); - const data = await response.json(); - setStatus(data); + // Determine which step we should be on + const determineStep = useCallback((status: EnhancedSetupStatus, eventStatus: EventStatus | null): SetupStep => { + // Check if models are ready (either from enhanced-status or events-status) + // The eventStatus.downloaded field provides a more reliable check when models are downloaded but not loaded + const clipReady = status.clip_status === 'ready' || eventStatus?.clip?.downloaded; + const textReady = status.text_embedding_status === 'ready' || eventStatus?.text_embedding?.downloaded; + + // Check if models are downloading or not ready + if (!clipReady || !textReady || + eventStatus?.clip?.downloading || + eventStatus?.text_embedding?.downloading) { + return 'models'; + } + + // Check if permission is needed (macOS only, on update) + if (status.needs_permission && status.needs_setup && status.platform === 'macos') { + return 'permission'; + } - // If setup not needed, go straight to complete - if (!data.needs_setup) { - setStep('complete'); + // Check if OCR migration is needed + if (status.migration_status?.needs_migration || + (eventStatus?.ocr_stats?.pending && eventStatus.ocr_stats.pending > 0)) { + return 'migration'; + } + + return 'complete'; + }, []); + + // Initial load + useEffect(() => { + const init = async () => { + try { + const setupStatus = await fetchStatus(); + const evtStatus = await fetchEventStatus(); + + if (setupStatus) { + const nextStep = determineStep(setupStatus, evtStatus); + setStep(nextStep); + } else { + // API returned null - show error + setError('Could not connect to backend. Is it running?'); + setStep('models'); // Show something rather than stuck loading + } + } catch (err) { + console.error('Setup init error:', err); + setError('Failed to load setup status. Check if backend is running.'); + setStep('models'); // Show something rather than stuck loading + } + }; + init(); + }, [fetchStatus, fetchEventStatus, determineStep]); + + // Poll for updates during active steps + useEffect(() => { + if (step === 'loading' || step === 'complete') return; + + const interval = setInterval(async () => { + const setupStatus = await fetchStatus(); + const evtStatus = await fetchEventStatus(); + + if (setupStatus) { + const nextStep = determineStep(setupStatus, evtStatus); + + // Auto-advance steps when done + if (step === 'models' && nextStep !== 'models') { + setStep(nextStep); + } else if (step === 'migration' && nextStep === 'complete') { + setStep('complete'); + } + } + }, 2000); + + return () => clearInterval(interval); + }, [step, fetchStatus, fetchEventStatus, determineStep]); + + // Auto-trigger model download when on models step + useEffect(() => { + if (step === 'models' && !downloadTriggered && status) { + // Check if any model needs downloading + const needsClip = status.clip_status !== 'ready'; + const needsBge = status.text_embedding_status !== 'ready'; + + if (needsClip || needsBge) { + setDownloadTriggered(true); + // Trigger download endpoint - this will download models + fetch('/api/v1/setup/download-models', { method: 'POST' }) + .then(async (response) => { + // The endpoint returns an SSE stream, but we don't need to read it + // The polling will pick up the status changes + console.log('Model download triggered'); + }) + .catch(console.error); } - } catch (err) { - console.error('Failed to fetch setup status:', err); } - }; + }, [step, downloadTriggered, status]); + + // Trigger sync when on migration step + useEffect(() => { + if (step === 'migration' && !syncTriggered && !eventStatus?.sync?.is_syncing) { + setSyncTriggered(true); + // Trigger sync to process OCR for existing screenshots + fetch('/api/v1/sync/start', { method: 'POST' }).catch(console.error); + } + }, [step, syncTriggered, eventStatus?.sync?.is_syncing]); const handleResetPermissions = async () => { - setStep('resetting'); setError(null); - try { const response = await fetch('/api/v1/setup/reset-permissions', { method: 'POST', }); const data = await response.json(); - - if (data.success) { - setStep('granting'); - } else { + if (!data.success) { setError(data.message || 'Failed to reset permissions'); - setStep('welcome'); } } catch (err) { setError('Failed to connect to backend'); - setStep('welcome'); } }; @@ -78,6 +222,13 @@ export default function SetupPage() { const isFirstRun = status && !status.last_seen_version; const isUpdate = status && status.last_seen_version && status.last_seen_version !== status.current_version; + // Calculate migration progress (guard against division by zero) + const migrationProgress = eventStatus?.ocr_stats + ? (eventStatus.ocr_stats.completed + eventStatus.ocr_stats.pending > 0 + ? (eventStatus.ocr_stats.completed / (eventStatus.ocr_stats.completed + eventStatus.ocr_stats.pending)) * 100 + : 100) + : status?.migration_status?.progress_percent ?? 0; + return (
@@ -86,7 +237,7 @@ export default function SetupPage() {

LiveRecall

{isFirstRun - ? "Welcome! Let's set up screen capture." + ? "Welcome! Let's get you set up." : isUpdate ? `Updated to v${status?.current_version}` : 'Setup'} @@ -95,83 +246,227 @@ export default function SetupPage() { {/* Step Content */}

- {step === 'welcome' && ( + + {/* Loading State */} + {step === 'loading' && ( +
+
+

Checking setup status...

+
+ )} + + {/* Models Step - Download Progress */} + {step === 'models' && ( <>

- Screen Capture Permission + Downloading Models

-

- LiveRecall needs screen capture permission to record your screen. - {isUpdate && ' After an update, macOS requires re-granting this permission.'} -

- Click the button below to reset permissions. You'll be prompted for your - admin password, then macOS will ask you to grant screen capture access. + LiveRecall uses AI models for image and text search. + This is a one-time download.

- {error && ( -
- {error} + {/* CLIP Model */} +
+
+ CLIP Vision Model + + {(status?.clip_status === 'ready' || eventStatus?.clip?.downloaded) ? 'Ready' : + eventStatus?.clip?.downloading ? 'Downloading...' : + status?.clip_status === 'downloading' ? 'Downloading...' : 'Pending'} +
- )} +
+
+
+

~1.7 GB - Required for image search

+
- - - )} + {/* Text Embedding Model */} +
+
+ Text Embedding Model + + {(status?.text_embedding_status === 'ready' || eventStatus?.text_embedding?.downloaded) ? 'Ready' : + eventStatus?.text_embedding?.downloading ? 'Downloading...' : + status?.text_embedding_status === 'downloading' ? 'Downloading...' : 'Pending'} + +
+
+
+
+

~130 MB - For text search

+
- {step === 'resetting' && ( -
-
-

Resetting permissions...

-

- Enter your admin password when prompted -

-
+ {/* OCR Status */} +
+
+ OCR Engine + + {(status?.ocr_status === 'ready' || eventStatus?.ocr?.available) ? 'Ready' : 'Not Available'} + +
+
+
+
+

+ {status?.platform === 'macos' ? 'Apple Vision (built-in)' : 'Tesseract'} +

+
+ +
+ {!(status?.clip_status === 'ready' || eventStatus?.clip?.downloaded) && ( + <> +
+

+ Downloading models automatically... +

+ + )} +
+ )} - {step === 'granting' && ( + {/* Permission Step */} + {step === 'permission' && ( <>

- Grant Permission + Screen Capture Permission

+

+ LiveRecall needs screen capture permission to record your screen. + {isUpdate && ' After an update, macOS requires re-granting this permission.'} +

-

Now grant screen capture permission:

    -
  1. A dialog will appear asking for Screen Recording permission
    2. -
  2. - Click "Open System Settings" or go to System Settings → Privacy & - Security → Screen Recording -
    3. +
  3. Click "Reset Permissions" below
    4. +
  4. Enter your admin password when prompted
    5. +
  5. Go to System Settings → Privacy & Security → Screen Recording
  6. Find "LiveRecall" and toggle it ON
    7. -
  7. You may need to restart LiveRecall
+ {error && ( +
+ {error} +
+ )} +
)} + {/* Migration Step - OCR Processing */} + {step === 'migration' && ( + <> +

+ Processing Existing Screenshots +

+

+ LiveRecall is extracting text from your existing screenshots to enable text search. + This runs in the background. +

+ + {/* Progress Bar */} +
+
+ OCR Processing + + {Math.round(migrationProgress || 0)}% + +
+
+
+
+
+ + {/* Stats */} +
+
+
+ {eventStatus?.ocr_stats?.completed ?? status?.migration_status?.screenshots_with_ocr ?? 0} +
+
Processed
+
+
+
+ {eventStatus?.ocr_stats?.pending ?? status?.migration_status?.screenshots_without_ocr ?? 0} +
+
Remaining
+
+
+ + {/* Current Phase */} + {eventStatus?.sync?.is_syncing && ( +
+
+ + {eventStatus.sync.current_phase === 'embedding' && 'Generating image embeddings...'} + {eventStatus.sync.current_phase === 'ocr' && 'Extracting text (OCR)...'} + {eventStatus.sync.current_phase === 'text_embedding' && 'Generating text embeddings...'} + {!eventStatus.sync.current_phase && 'Processing...'} + +
+ )} + + {status?.migration_status?.estimated_time_minutes && migrationProgress < 100 && ( +

+ Estimated time remaining: ~{Math.round(status.migration_status.estimated_time_minutes * (100 - migrationProgress) / 100)} minutes +

+ )} + + +

+ Processing will continue in the background +

+ + )} + + {/* Complete Step */} {step === 'complete' && (
@@ -201,15 +496,12 @@ export default function SetupPage() { )}
- {/* Skip option */} - {(step === 'welcome' || step === 'granting') && ( -
- + {/* Step indicators */} + {step !== 'loading' && step !== 'complete' && ( +
+
+
+
)}
diff --git a/web/src/lib/api.ts b/web/src/lib/api.ts index ebdf429..1fcb400 100644 --- a/web/src/lib/api.ts +++ b/web/src/lib/api.ts @@ -14,6 +14,7 @@ import type { VisibilityFilter, IncognitoStatus, BulkOperationResponse, + SearchMode, } from '@/types'; const API_BASE = '/api/v1'; @@ -77,7 +78,8 @@ export async function search( safeModeLevel?: string, startDate?: string, endDate?: string, - visibility: VisibilityFilter = 'visible_only' + visibility: VisibilityFilter = 'visible_only', + searchMode: SearchMode = 'auto' ): Promise { return fetchApi('/search', { method: 'POST', @@ -89,6 +91,7 @@ export async function search( start_date: startDate, end_date: endDate, visibility, + search_mode: searchMode, }), }); } @@ -104,6 +107,7 @@ export async function searchWithParams(params: SearchParams): Promise { + return fetchApi(`/screenshots/${screenshotId}/ocr`); +} + // Timeline export async function getDateRange(visibility: VisibilityFilter = 'visible_only'): Promise { return fetchApi(`/screenshots/date-range?visibility=${visibility}`); diff --git a/web/src/types/index.ts b/web/src/types/index.ts index 837d0aa..b6d5a10 100644 --- a/web/src/types/index.ts +++ b/web/src/types/index.ts @@ -139,6 +139,9 @@ export interface DensityResponse { max_date: string | null; } +// Search mode options +export type SearchMode = 'auto' | 'image' | 'text_fuzzy' | 'text_semantic'; + // Search with date filters export interface SearchParams { query: string; @@ -148,6 +151,7 @@ export interface SearchParams { start_date?: string; end_date?: string; visibility?: VisibilityFilter; + search_mode?: SearchMode; } // Bulk operations diff --git a/web/tsconfig.tsbuildinfo b/web/tsconfig.tsbuildinfo index df954dc..ac1a809 100644 --- a/web/tsconfig.tsbuildinfo +++ b/web/tsconfig.tsbuildinfo @@ -1 +1 @@ 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