hister

You can now index and search your own web history and local files privately without relying on external search engines.

Hister, a new open-source project hosted on GitHub, offers users a private, self-hosted search engine designed to index visited web pages and local files. The tool emphasizes privacy by keeping all indexing and search functions local, without mandatory cloud services or telemetry. Users can install Hister by downloading a binary and running it locally, then use a browser extension for Firefox or Chrome to automatically index visited pages. The project supports full-text indexing, powerful query capabilities including field filters and wildcards, and an optional semantic search feature that can be configured with a user-provided embeddings endpoint. Hister also allows for importing browser history, indexing local directories, and supports multiple installation methods like Homebrew, Docker, and Nix. It provides web, terminal, and MCP clients for versatile access and includes multi-user support for shared server environments.

Hister's approach to a personal search engine challenges the centralized model of large search providers by empowering users with control over their data and search index. Its focus on privacy and local operation is particularly relevant in an era of increasing data surveillance and commodification. The optional integration with user-configured semantic search endpoints suggests a modular architecture that can adapt to future advancements in AI and NLP, allowing users to leverage cutting-edge technology without compromising their data privacy.

The project's architecture, supporting web, terminal, and AI assistant integrations, positions it as a versatile tool for a range of users, from individuals seeking better personal knowledge management to developers looking for integrated search solutions. The multi-user support further expands its applicability, making it feasible for small teams or families to maintain a shared, private knowledge base. Future developments may focus on enhancing the AI assistant integration and expanding the range of supported data sources for indexing.