Do we really need a vector database for every AI documentation chatbot?
Been seeing a lot of discussions around RAG, embeddings, and vector databases lately. That made me wonder, do we really need a vector database for every AI documentation chatbot? So I explored Google's Open Knowledge Fo
Been seeing a lot of discussions around RAG, embeddings, and vector databases lately.
That made me wonder, do we really need a vector database for every AI documentation chatbot?
So I explored Google's Open Knowledge Format (OKF) and built Ask-Your-Markdown to better understand where this approach fits. I also wrote a blog sharing what I learned. Both the article and the GitHub repository are mentioned below.
If you've been curious about OKF, I hope this helps.
imelanthirayan
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Ask-Your-Markdown
Ask questions about a folder of Markdown files using AI or offline search. Built on Google's Open Knowledge Format (OKF).
π Ask Your Markdown
Ask questions about a folder of markdown files - and get answers grounded in your documents.
No database. No embeddings. No vendor lock-in. Just your Markdown files, organized with OKF and ready to browse - with or without an LLM.
Built on the Open Knowledge Format (OKF) - an open, vendor-neutral standard from Google Cloud.
Works with OpenAI Β· Azure AI Foundry Β· or fully offline.
π§ Table of contents
- Why this exists
- How it works
- How this compares to classic RAG
- The two chapters: Build β Ask
- What's in the box
- Quick start
- Chapter 1 - Build a bundle
- Chapter 2 - Ask questions
- Labels: the fast path
- Big documents
- Try it with no API key
- Why people like this
- What's OKF vs. this sample
- How well does it work - honestly
π‘ Why this exists
Imagine a drawer full of notes -β¦
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes β full credit and traffic to the original publisher.