knowlp-rag

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type: KnowLP document

status: engine

date: "2026-08-29"

note: engine README (v3.0.8 repo edition, dsh-first)

---

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)

Use from any MCP client

A .mcp.json ships at the repo root — copy it next to your project (or into your host's config), then provide the two required env vars (KNOWLP_VAULT, KNOWLP_GRAPH_DIR; the file reads them via ${...}, so setting them in your shell is enough).

{
  "mcpServers": {
    "knowlp": {
      "type": "stdio",
      "command": "npx",
      "args": ["--yes", "--package", "@eqman00003/knowlp-rag", "knowlp-mcp"],
      "env": { "KNOWLP_VAULT": "${KNOWLP_VAULT}", "KNOWLP_GRAPH_DIR": "${KNOWLP_GRAPH_DIR}" }
    }
  }
}

Two optional env vars matter when your notes live outside a plain-vault setup:

• KNOWLP_SKILL_INDEX — path to a skill index (enables skill_search);
• HERMES_HOME — only needed for the chroma skill index when your agent home is not ~/.hermes; the chroma DB resolves to $HERMES_HOME/skills/.chroma/chroma.sqlite3.

The config shape is verified over stdio (initialize → tools/list → a real knowlp_search); any stdio MCP host works.

Try the demo vault (no private data, no embedding model)

A 7-note bilingual demo vault ships with the repo. From clone to first search:

POSIX:

pip install -e .
export KNOWLP_VAULT="$PWD/examples/demo-vault"
export KNOWLP_GRAPH_DIR="$PWD/.demo-graph"
python build_graph.py && python -m vector_index --build
python knowlp_search.py "How should I understand this RAG architecture?" --hybrid

Windows PowerShell:

pip install -e .
$env:KNOWLP_VAULT = "$PWD\examples\demo-vault"
$env:KNOWLP_GRAPH_DIR = "$PWD\.demo-graph"
python build_graph.py; python -m vector_index --build
python knowlp_search.py "How should I understand this RAG architecture?" --hybrid

Five bilingual verification queries and what each demonstrates:

[docs/demo.md](docs/demo.md). Agent onboarding instructions:

[examples/agent-setup.md](examples/agent-setup.md).

Seven tools