Precision File Search
- Android app
- Not listed
- Free plan
- Yes
- Runs on
- Linux, Mac, Windows
Summary
Precision File Search is a free desktop application for searching, 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 summaries. Its trainable machine-learning model can sort scattered documents into categories tailored by the user. 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. An internet connection is needed on first run to download multilingual AI models; later use can be offline. The project lists 18 languages, including English, Arabic, Chinese, French, German, Hindi, Japanese, and Spanish. It also supports OpenAI-compatible inference URLs, including online providers and local engines such as Ollama and LM Studio. The README says the core platform runs on most modern consumer hardware with at least 8 GB of RAM. Local LLM use calls for a dedicated GPU, with at least 4 GB of VRAM for smaller models. The project is licensed under MPL-2.0.
Who it is for
PFS suits people who search and organize substantial local document collections, including researchers, students, developers, legal and business professionals, and writers. It is also intended for users seeking offline file work after the initial model download.
What is good
- Searches, classifies, and answers questions about local files
- Can run offline after downloading models
- Lists support for 18 languages
- Supports local inference engines including Ollama and LM Studio
What to know first
- First-run model download needs an internet connection
- Local LLM use requires a dedicated GPU
- Core platform recommendation includes at least 8 GB RAM
Verdict
Precision File Search brings keyword-style, semantic, and question-based discovery to local documents. Its first-run download and hardware needs are worth checking before choosing it.
Precision File Search plans and pricing
All plansCompared on desktop search software
- Free plan
- Yespfs-ai.github.io
- Supported platforms
- Windows, macOS, Linuxpfs-ai.github.io
- Index locations
- localpfs-ai.github.io
- Content search
- Yespfs-ai.github.io
Facts
- What it does
- Precision File Search is a desktop application for searching, classifying, and understanding local files.pfs-ai.github.io · 3 Oct 2026
- Search modes
- It combines high-speed classic search, semantic search using a RAG pipeline, and natural-language questions with summarized answers.pfs-ai.github.io · 3 Oct 2026
- Classification
- Its trainable machine-learning model can classify scattered documents into user-tailored categories.pfs-ai.github.io · 3 Oct 2026
- Local operation
- The maker says the application and AI models can run entirely on the local machine, and that it works offline without cloud dependency.pfs-ai.github.io · 3 Oct 2026
- Privacy
- The maker says PFS never uploads files or search queries and that personal data is not sent to external servers.pfs-ai.github.io · 3 Oct 2026
- Security
- The maker lists path-traversal prevention, XSS sanitization, and prompt-injection hardening as security measures.pfs-ai.github.io · 3 Oct 2026
- Languages
- The site lists support for 18 languages, including English, Arabic, Chinese, French, German, Hindi, Japanese, and Spanish.pfs-ai.github.io · 3 Oct 2026
- Intended users
- The project README names researchers, students, developers, legal and business professionals, writers, and people organizing large local document collections as intended users.github.com · 3 Oct 2026
- Windows download
- The project README directs users to download a Windows installer with an .exe extension.github.com · 3 Oct 2026
- AI model setup
- The release page says an internet connection is needed on first run to download multilingual AI models, after which subsequent runs can be fully offline.github.com · 3 Oct 2026
- AI integrations
- The README says PFS supports OpenAI-compatible inference URLs, including online providers such as OpenAI and Together.ai and local engines such as Ollama and LM Studio.github.com · 3 Oct 2026
- Hardware requirements
- The README says the core platform runs on most modern consumer hardware with at least 8 GB of RAM; local LLM use is listed as needing a dedicated GPU, with at least 4 GB VRAM for smaller models.github.com · 3 Oct 2026
- License
- The project README states that PFS is licensed under the Mozilla Public License 2.0 (MPL-2.0).github.com · 3 Oct 2026
- Business services
- The README offers organizations custom feature development, enterprise integrations, priority support and maintenance, on-premise deployment consulting, and AI/RAG consulting.github.com · 3 Oct 2026
- Maintainer
- The README says PFS was developed by Aran Kazemi and describes him as an IBM Certified AI Engineer.github.com · 3 Oct 2026
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Sources
- pfs-ai.github.io/PFS/· checked 3 Oct 2026
- github.com/PFS-AI/PFS· checked 3 Oct 2026
- github.com/PFS-AI/PFS/releases/tag/v1.1.1· checked 3 Oct 2026