Building Knemora: An Open-source, Local-first AI (SI) Second Brain

Описание и применение

Knemora превращает документы и наборы данных в связанный набор знаний, позволяя визуализировать связи и задавать вопросы без отправки данных в облако.

Задача

Need to convert documents and datasets into connected knowledge, explore relationships visually, and query without sending data to cloud inference services.

Результат

Knemora enables local, privacy‑preserving knowledge management and inference, converting raw documents into a searchable, graph‑based second brain.

Рабочий процесс

• 1. Import documents and datasets into Knemora.
• 2. Knemora parses and stores content in Markdown pages with frontmatter and explicit links.
• 3. The system builds a graph view of connections using Sigma.js/Graphology.
• 4. Local inference is performed with llama.cpp and Qwen3‑8B Q3_K_M.
• 5. Retrieval uses BM25 search and bounded graph navigation without a vector database.
• 6. Structured calculations are performed with Polars, keeping results grounded in computation.
• 7. Users query the knowledge base via search, graph navigation, or chat, receiving source‑referenced answers.

Доказательства

I bring your documents and datasets; Knemora compiles knowledge into a portable Markdown wiki, connects pages, and makes that knowledge available through search, a native graph, and chat with source citations.

Источник

Local inference: llama.cpp with Qwen3-8B Q3_K_M as the default model.

Источник

Retrieval: BM25 search and bounded graph navigation, without a vector database.

Источник

The stack includes Vue 3, FastAPI, Google ADK, SQLite, and Sigma.js/Graphology.

Источник