@dsh-external/dsh-kb-rag

Local-first RAG knowledge tools for DSH — thin tool wrapper around the kb-rag Python pipeline (Ollama bge-m3 + ChromaDB + SiYuan). Four tools, all on-premises, zero API cost, your documents never leave the machine.

Tool	What it does	Backing script
kb_query	Semantic search over your local vector DB (top-k chunks with source + similarity)	query.py --json
kb_ingest	Ingest a docs directory into ChromaDB (incremental, manifest hash-idempotent; Shiyu notes auto-stripped of front-matter + machine layer)	ingest.py
kb_crawl	Batch-crawl URLs to clean Markdown (Scrapling + markdownify)	crawl.py
kb_related	Related-document recommendations: retrieval-style, graph-style knowledge walk, or from a known doc	related.py

The plugin is a pure forwarder: it locates Python, spawns the kb-rag scripts and parses their output. All knowledge-base logic lives in the Python project, which you can also use standalone.

Install

# requires a dsh build with the `dsh plugin` subcommand; 
 = tui/headless/web/...
dsh plugin --profile 
 add github:YuMu247/dsh-kb-rag
dsh --profile 
        # restart: kb_query / kb_ingest / kb_crawl / kb_related injected

Peer dependencies (@deepseek-ai/dsh-tools, @deepseek-ai/cordis) are provided by the dsh profile.

Prerequisites (one-time, on the host)

# 1. the Python pipeline
git clone https://github.com/YuMu247/kb-rag
pip install -r kb-rag/requirements.txt

# 2. Ollama + embedding model
ollama pull bge-m3        # ~1.2GB

# 3. tell the plugin where kb-rag lives (either one)
#    a) plugin config:  kbRagDir: "/path/to/kb-rag"
#    b) env var:        KB_RAG_DIR=/path/to/kb-rag
#    c) default:        ./kb-rag under the session working directory

# 4. ingest something, then ask
python kb-rag/ingest.py --docs /path/to/docs

Optional (Windows daily sync): examples/sync_daily.cmd in the kb-rag repo starts Ollama, syncs Shiyu video notes, ingests and imports into SiYuan.

Tool arguments

• kb_query — query (required), k (default 5, max 20). Returns .