u/WritHerAI

I’ve been experimenting with Graph RAG for personal knowledge systems and ended up building a small open-source project called Kwipu.

The idea was to move beyond classic:

•	chunking

•	embedding search

•	top-k retrieval

and instead preserve relationships between notes/entities from Markdown and Obsidian vaults.

Current features:

•	Markdown + Obsidian wikilink parsing

•	local Ollama inference

•	entity/relation extraction

•	knowledge graph construction

•	hybrid retrieval (BM25 + embeddings)

•	local-first architecture

A few things I learned while building it:

•	plain vector search is pretty weak for interconnected notes

•	Obsidian vaults already contain implicit graph structure

•	hybrid retrieval helps a lot with technical notes/acronyms

•	local models struggle with reliable entity extraction unless you use decent embeddings/models

It’s still early-stage and definitely experimental, but I’m curious how other people here are approaching:

•	Graph RAG

•	agent memory

•	semantic PKM

•	local-first knowledge systems

Especially interested in whether people found graph traversal actually improves retrieval quality in practice, or if most setups still converge back to “good embeddings + reranking”.

I’ve been experimenting with Graph RAG for personal knowledge systems and ended up building a small open-source project called Kwipu.

The idea was to move beyond classic:

•	chunking

•	embedding search

•	top-k retrieval

and instead preserve relationships between notes/entities from Markdown and Obsidian vaults.

Current features:

•	Markdown + Obsidian wikilink parsing

•	local Ollama inference

•	entity/relation extraction

•	knowledge graph construction

•	hybrid retrieval (BM25 + embeddings)

•	local-first architecture

A few things I learned while building it:

•	plain vector search is pretty weak for interconnected notes

•	Obsidian vaults already contain implicit graph structure

•	hybrid retrieval helps a lot with technical notes/acronyms

•	local models struggle with reliable entity extraction unless you use decent embeddings/models

It’s still early-stage and definitely experimental, but I’m curious how other people here are approaching:

•	Graph RAG

•	agent memory

•	semantic PKM

•	local-first knowledge systems

Especially interested in whether people found graph traversal actually improves retrieval quality in practice, or if most setups still converge back to “good embeddings + reranking”.

I am conducting a study to gauge user satisfactionwith such applications in the OneNote ecosystem. Thanks

Hey everyone! I wanted to share a small tool I’ve been building called Writher.

The idea is simple: it lives in your system tray and gives you two things.

Hold AltGr anywhere (any app, any text field) and just speak. It transcribes your voice with Whisper and pastes the text right where your cursor is. No clicking, no switching apps.

Hold Ctrl+R and you get a voice assistant that understands natural language. You can say things like “remind me to call Marco in one hour” or “appointment with the dentist tomorrow at 3pm” and it handles the rest. Notes, to-do lists, shopping lists, reminders with toast notifications, all stored locally in SQLite.

The part I’m most proud of: everything runs 100% offline. Speech recognition via faster-whisper, intent parsing via Ollama, no cloud, no API keys, no telemetry. Once you download the models it works with no internet at all.

There’s also a little animated floating widget with eyes that react to what it’s doing (listening, thinking, error…) which is silly but I kind of love it.

It’s Python, MIT license, Windows 10/11 only for now.

GitHub: https://github.com/benmaster82/writher

Would love feedback, especially from anyone who uses voice input regularly. Still early days but it works well for my daily workflow!