{"repo":"anshupriyan/Local-Recall","free":true,"listed":false,"github":"https://github.com/anshupriyan/Local-Recall","clone":"git clone https://github.com/anshupriyan/Local-Recall.git","description":"A 100% local, privacy-first alternative to Windows Recall — captures your screen, OCRs it, and lets you semantically search and chat with your history using a local LLM. Zero cloud, zero data leakage.","language":"Python","stars":17,"topics":["ai","microsoft","python","windows","windows-recall","embeddings","knowledgebase","llm","lm-studio","local-ai"],"license":"MIT","category":"ai-agents","readme_excerpt":"Local Recall A 100% local, privacy-first alternative to Windows Recall — captures your screen, OCRs it, and lets you semantically search and chat with your history using a local LLM. Zero cloud, zero data leakage. Project Status: Early Prototype under Active Development This repository is an early proof-of-concept and is under active development. APIs, database schemas, and workflows are subject to change. --- Technical Pipeline Architecture Local Recall is built on an asynchronous pipeline structured across five key phases: 1. Capture & Deduplication : Captures screen snapshots every 5 seconds using mss . Compares the perceptual hash ( imagehash + Pillow ) of the current frame against the previous one. Static screens are skipped to save disk space and database footprint. 2. Local OCR (Out-of-Band) : A background worker ( ocr worker.py ) polls the SQLite database for unprocessed snapshots and extracts text using Windows' built-in high-performance WinRT OCR Engine ( Windows.Media.Ocr ). 3. Local Vector Embeddings : A background worker ( embedding worker.py ) extracts OCR texts, generates 384-dimensional vector representations using sentence-transformers ( all-MiniLM-L6-v2 ), and serializes them to float32 byte buffers. 4. Vector Database : Stores vector embeddings in a virtual SQLite vector table ( snapshot vectors ) powered by the lightweight sqlite-vec extension, enabling sub-50ms K-Nearest Neighbor (KNN) semantic searches locally. 5. Retrieval-Augmented Generation (RAG) : I","default_branch":null,"files":null,"tree":[],"storefront":"/r/anshupriyan","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/anshupriyan/Local-Recall/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}