type: KnowLP文档 文档状态: 引擎 日期: "2026-08-29" 说明: 引擎 README（v3.0.8 仓库版，dsh 优先）
KnowLP-RAG
Agent-first knowledge retrieval — turn your Markdown notes into a self-maintaining knowledge graph that is "use it or lose it". Agents (DSH / Claude Code) install, build the graph, and self-check it; only the vault path must be provided by the human. Retrieval returns reading paths: which notes to read, in what order, and which are similar substitutes.
Quick start (3 steps)
All three steps are agent-runnable; only KNOWLP_VAULT (your notes directory) must be provided by the human.
# 1. Install (official npm registry)
dsh plugin add "@eqman00003/knowlp-rag"
# 2. Set the two required env vars (without them the dual-graph engine idles and only full-text search works)
export KNOWLP_VAULT="$HOME/Notes" # your Markdown notes directory
export KNOWLP_GRAPH_DIR="$HOME/.knowlp-dsh" # writable index directory
# 3. Restart dsh web — the first search triggers Python env bootstrap (~30s, don't interrupt)Six tools
| Tool | Purpose |
|---|---|
knowlp_search | Four-engine fan-out retrieval (dual-graph P/S-Agent + vector + full-text) |
knowlp_get_note | Read note content (read-only, path-traversal safe) |
knowlp_stats | Engine/graph health self-check (first stop for troubleshooting) |
knowlp_record_feedback | Explicit feedback (the only entry point of the weight loop) |
knowlp_record_correction | Explicit preference pairs (chosen ≻ rejected) — the input to preference learning |
skill_search | Skill index retrieval |
PixelRAG (optional cross-machine visual retrieval)
PixelRAG is an optional visual-retrieval engine — it embeds visual content for retrieval instead of relying on text tokens alone. It runs on a separate GPU machine on your network: the agent offloads the visual-embedding work to that box over Tailscale rather than computing it on the laptop. Retrieval falls back through three tiers:
- desktop GPU — the primary dedicated box (an RTX-class machine reachable over Tailscale)
- local — a same-machine fallback
- cloud API — a hosted PixelRAG endpoint
Configure it via KNOWLP_PIXELRAG_DESKTOP / pixelrag_local. Unconfigured, it stays off — retrieval still works in n-gram / embedding mode.
Reproducing this: it is deployment-specific — you need your own GPU machine running a PixelRAG service, a network path to it (e.g. Tailscale), and its endpoint address. No bundled service ships with KnowLP.
Documentation
- Install & usage guide: docs/usage.md
- Troubleshooting: docs/troubleshooting.md
- dsh integration details (env vars / Cordis plugin): dsh/README.md
Why KnowLP?
Grep for "RAG architecture" gives you 105 files. KnowLP gives you 3 ranked hits with dependency context.
grep | Naive vector store | KnowLP | |
|---|---|---|---|
| Result ranking | ❌ | ✅ | ✅ |
| Dependency chain (P-Agent) | ❌ | ❌ | ✅ |
| Similar substitutes (S-Agent) | ❌ | ❌ | ✅ |
| Works without GPU | ✅ | ❌ | ✅ (n-gram mode) |
| Improves with use (feedback) | ❌ | ❌ | ✅ (weight loop) |
| Paragraph-level matching | ❌ | ❌ | ✅ |
| Decay & forgetting (use it or lose it) | ❌ | ❌ | ✅ (three half-life tiers) |
The difference: vector search finds documents that "contain keywords"; KnowLP finds documents you *should read given your query*, with reading paths. Edge weights between notes evolve with usage — consumed edges strengthen, unused edges decay by half-life (ephemeral 1 day / default 30 days / declarative never).
Works with Chinese note vaults out of the box (Chinese time-anchor queries and Chinese full-text search are supported).
Demo
$ knowlp_search "RAG architecture"
1. [HIT] RAG Architecture.md (score 0.77)
2. [LINK] Vector Database Selection.md (score 0.61) ← prerequisite chain
3. [LINK] Retrieval Eval Pitfalls.md (score 0.42)
4. [LINK] _Index-Reading Order (depth 1) ← tells you where to start reading
5. [LINK] _Index-Related Concepts.md (depth 2)Local development
git clone https://github.com/wly8691-jpg/knowlp-rag.git
cd knowlp-rag
pip install -e . # provides knowlp-mcp / knowlp-build / knowlp-search
# configure vault in config.yaml → build graph → search
python build_graph.py
python knowlp_search.py "RAG architecture"