obsidianrag is an open-source Python package that implements a Retrieval-Augmented Generation system specifically for Obsidian vaults. It leverages SQLite FTS5 for full-text search and LanceDB for vector embeddings to enable semantic querying of personal notes. The tool integrates with local LLMs such as those from Ollama and is designed for developers and power users who want to chat with their personal knowledge base.
obsidianrag is an AI & ML project. Efficiently searching and retrieving information from large collections of Obsidian markdown notes using semantic search. It is built as an open-source project for developers and knowledge workers using Obsidian. obsidianrag is open source under the MIT license. It runs on the command line and API, and it can be self-hosted.
It is developed by Vasallo94, and it first shipped in 2024. The project is developed in the open on GitHub with 113 stars and 31 commits in the last 90 days. Key capabilities include RAG Querying, SQLite FTS5, and LanceDB Integration.
Summary written by a language model from the project’s public pages.
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