Caelune FAQs

Question 1

How does Caelune ensure the privacy of my documents?

Accepted Answer

Caelune is built with a 'local-first' philosophy, meaning all your data, indexes, models, and configuration files remain stored exclusively on your computer. Network access is only required for user-initiated downloads (like Runtime components or models), and retrieved text only leaves the app if you explicitly copy it or an MCP client securely requests it.

Question 2

What types of documents can Caelune index and search?

Accepted Answer

Caelune offers broad document support, including Markdown files (with awareness for Logseq properties), PDF documents, and a wide array of other formats parseable by Apache Tika (e.g., DOCX, XLSX, TXT). It provides unified search results across all indexed document types.

Question 3

What is Caelune and what does it do?

Accepted Answer

Caelune is a Windows desktop application designed for local-first retrieval and semantic search across your private Markdown, PDF, and Tika-backed documents. It builds searchable indexes from your existing files, combining lexical and semantic search to find relevant information quickly. It also offers a read-only Model Context Protocol (MCP) server for secure integration with AI clients.

Question 4

How does Caelune perform its document searches?

Accepted Answer

Caelune utilizes a sophisticated hybrid retrieval system. It combines FTS5 lexical (exact text) search with LanceDB semantic vector search, structural signal analysis, and optional `BAAI/bge-reranker-v2-m3` reranking to deliver highly relevant results. This ensures both precision and conceptual understanding in your queries.

Question 5

Can Caelune integrate with AI clients or large language models (LLMs)?

Accepted Answer

Yes, Caelune includes a read-only Model Context Protocol (MCP) server. This allows compatible AI clients to securely query your local knowledge bases for Retrieval Augmented Generation (RAG) purposes, providing factual context to AI models without ever exposing your private data to external services unnecessarily.

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