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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.

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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:

1. desktop GPU — the primary dedicated box (an RTX-class machine reachable over Tailscale)
2. local — a same-machine fallback
3. 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"

View Architecture Diagram