Background
Obsidian’s graph and backlink system shows the value of understanding relationships across local notes. Notion demonstrates how useful AI becomes when it can work with broader workspace context. Zettlr also shows that research users benefit from connecting notes, references, and documents.
Proposal
Add directory-level context so Markdown Reader’s AI can answer questions based on local notes in a selected folder.
Expected Outcome
Users should be able to ask questions such as:
- “Based on my notes in this folder, what did I say about Project X?”
- “Summarize all notes related to this topic.”
- “Find related notes for this section.”
- “Generate a research summary from this folder.”
Suggested Implementation
- Let users choose a local folder as a knowledge base.
- Index Markdown/text files locally.
- Start with simple keyword search or lightweight embeddings.
- Optionally evaluate ChromaDB or another local vector store.
- Keep all indexing and retrieval local by default.
Value
This combines Obsidian-style local knowledge management with privacy-respecting AI workflows, giving Markdown Reader a stronger unique selling point.
Background
Obsidian’s graph and backlink system shows the value of understanding relationships across local notes. Notion demonstrates how useful AI becomes when it can work with broader workspace context. Zettlr also shows that research users benefit from connecting notes, references, and documents.
Proposal
Add directory-level context so Markdown Reader’s AI can answer questions based on local notes in a selected folder.
Expected Outcome
Users should be able to ask questions such as:
Suggested Implementation
Value
This combines Obsidian-style local knowledge management with privacy-respecting AI workflows, giving Markdown Reader a stronger unique selling point.