KaaS — Knowledge as a Service

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Turn scattered notes, documents, and transcripts into a searchable, queryable personal Wiki — powered by LLM-driven knowledge compilation.

Documentation · Quick Start · MCP access

What Makes This Different

Unlike typical RAG systems that chunk and embed raw text, KaaS compiles your content through a 4-phase LLM pipeline. What comes out is human-readable Markdown articles — not a black-box vector store.

The Compiled Wiki

What you get is a tree of Markdown articles: categories on the left, the rendered article in the middle, its headings down the right. Chat answers cite these articles by name, so an answer and the page it came from are one click apart.

How It Works

Raw content passes through four LLM phases: extract concepts, entities and decisions; classify them into articles; write or merge the Markdown; update the indexes. Retrieval then reads whole pages rather than embedded fragments — the LLM walks the master index, picks the articles it needs, and answers from their full text.

Why We Built This

KaaS started as an internal tool. Our knowledge lived scattered across documents, meetings, and email — and every time someone changed roles or left, the context they'd built up walked out with them. New people spent weeks piecing it back together.

A distillation pipeline fixed that. It compiles each person's scattered material into a wiki tied to their role rather than their identity — so when someone moves on, the raw data goes but the distilled judgment stays for whoever fills the seat next.

The payoff is the same either way: the organization stops re-answering the same questions. That's what convinced us it was worth open-sourcing.

Quick Start

KaaS calls LLMs through any OpenAI-compatible API (OpenAI, DeepSeek, Ollama, vLLM, Azure OpenAI, etc.). Pick one of the three paths below.

Option A: Your AI agent

Already living in a coding agent (Claude Code, Codex, openclaw, …)? Give it this repository's URL and ask for what you want — it can do the whole setup. Or paste this to save it a step:

Set up KaaS to build a queryable knowledge base from my files.
Fetch https://raw.githubusercontent.com/bybit-exchange/kaas/main/docs/agent-quickstart.md
and follow it exactly.

If you are an AI agent reading this: fetch docs/agent-quickstart.md and follow that instead of the shell snippets below. It routes the three things users ask for — build a wiki from their files, run the full service with the web UI, or point your ask tool at a KaaS that already exists — and marks where to stop and ask them.

That snippet asks for one specific outcome: the AI engine and a queryable wiki, no web UI. Ask your agent for the web UI instead and the runbook sets up the full service — which is the same ground Options B and C cover, written for a human to run by hand.

Option B: Docker

docker run -d --name kaas \
  -p 8080:8080 \
  -v ./data:/app/data \
  -e LLM_API_KEY=sk-xxx \
  -e LLM_BASE_URL=https://api.openai.com/v1 \
  -e LLM_MODEL=gpt-4o-mini \
  ghcr.io/bybit-exchange/kaas:edge