Open Semantic Desktop Search vs Precision File Search

Open Semantic Desktop Search is free, open-source software for full-text and semantic search, text analytics and mining across large document sets. Its desktop edition comes as a virtual machine appliance for import into VirtualBox; configured shared folders point to documents, and indexing starts when the machine launches. Search supports wildcards, Boolean AND/OR/NOT and fuzzy matching. Multilingual semantic search uses a thesaurus to find synonyms, narrower terms and aliases, while stemming helps find other word forms. Interactive facets group metadata such as authors, organizations, people, dates, tags and document types. Previews can display PDFs, extracted text, table rows and images. Analysis tools include date charts, word clouds, word lists, network graphs and interactive maps. OCR can recognize text in images and scanned PDFs. Connectors include files, feeds, websites, SQL databases, MediaWiki and IMAP email. The desktop appliance is for a single user; a separate server installation is aimed at organizations and research teams. The multilingual virtual machine image is a 4 GB download, and the German desktop image is listed as coming soon.

Precision File Search is a free desktop application for finding, classifying, and understanding local files on Windows, macOS, and Linux. It combines classic search with semantic search using a retrieval-augmented generation pipeline, and can answer natural-language questions with summarized responses. A trainable machine-learning model sorts scattered documents into categories users define. The maker says the application and AI models can operate locally, without cloud dependency, and that files, queries, and personal data are not sent to external servers. The project lists support for 18 languages, including English, Arabic, Chinese, French, German, Hindi, Japanese, and Spanish. An internet connection is needed on first run to download multilingual AI models; later runs can be offline. PFS supports OpenAI-compatible inference URLs, including online providers and local engines such as Ollama and LM Studio. The README lists 8 GB of RAM for the core platform and a dedicated GPU, with at least 4 GB of VRAM for smaller models, for local LLM use. The project is licensed under MPL-2.0.