dsh-kb

dsh-kb

A personal knowledge base for AI agents: the source of truth lives on shared storage, each machine keeps a mirror and an index, retrieval is fast and works offline.

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Who it is for, what it costs, and how to try it

For one person (or a small team) whose knowledge already lives in a Markdown folder — on a network share, a NAS, or just a directory — and whose agent keeps re-reading it. Not a hosted product, not a multi-tenant service, not a note-taking UI: one machine, one bundle, one owner.

What it costs: Python ≥ 3.10 and no dependencies — no daemon, no server, no database service, no root. One process per command. State (mirror + index) lives under ~/.dsh-kb and runs about 1.9× the size of the bundle; the index is local, so a share that is offline or slow costs you freshness, not availability.

Try it in two minutes — no share and no config file needed:

git clone https://github.com/LUgABeEEE/dsh-kb && cd dsh-kb
bash examples/demo.sh          # builds a throwaway bundle, indexes it, searches it

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The problem

Personal knowledge — notes, lessons learned, decisions, project records — usually ends up scattered across session transcripts, a memory plugin, a pile of Markdown reports and a network share. So every agent session does the same thing: it re-reads several sources, stuffs raw text into the context window (one measured deployment re-sent ~230 000 tokens of context per request), and when it still cannot find the answer it says so.

dsh-kb collapses that into one path:

one Markdown directory on shared storage     ← single source of truth
        │  incremental pull
        ▼
local mirror + local FTS5 index              ← milliseconds, works offline
        │  two narrow tools
        ▼
the agent asks for "path + line + 240-char snippet"

The size of a result is independent of the size of the corpus — 200 pages or 10 000 pages, a search returns roughly 2.4 KB. That property is the whole point: growing the knowledge base does not make each lookup more expensive.

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Core ideas

#	Idea	Why
1	One source of truth, a derived mirror	The mirror is never an authority, so merge conflicts cannot happen; refreshing is a pure function of (remote listing, manifest) — interruptible and cheap to run often
2	The index is local and never on the share	SQLite's locking is not reliable on network filesystems; the index is a disposable derivative that can always be rebuilt
3	Privacy zones compiled into code	A sensitive zone is not even copied to disk, is not indexed, and is not returned by a default query — three layers, no conventions required
4	Proposal-based writes	An agent only writes to _incoming/; promotion is done by a human or a scheduled job — this prevents "a dozen contradictory near-duplicates about one topic"
5	Precision first, recall second	A query starts as a strict AND; only when that is empty does it relax to OR, and then generic terms are dropped first — otherwise a single shared word such as UNIQ drags in unrelated documents

Rationale and measurements: docs/design.md.

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Install

Standard library only, no third-party dependencies (Python ≥ 3.10):

pip install -e .          # or simply PYTHONPATH=src python3 -m dsh_kb
dsh-kb --version

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Quick start

1. Create a bundle (a directory on shared storage)

<share>/kb/
  index.md                 # optional: progressive-disclosure front door
  log.md                   # optional: change log
  z0-public/               # public zone
  z1-restricted/           # restricted zone
  z2-sensitive/            # sensitive zone (not mirrored, not indexed)
  _incoming/               # proposals (the only place an agent writes)
  _meta/                   # conventions, registry, receipts

2. Configure

Configuration lives in ~/.dsh-kb/config.json — deliberately outside any checkout, so a public clone never carries your paths or private data:

{
  "source": "auto",
  "mount": "/mnt/nas/kb",
  "local_root": "/srv/kb-share",
  "webdav_root": "https://nas.example.com:5000/webdav/kb",
  "stale_minutes": 15
}

source=auto picks the first usable option in the order mount → local directory → Samba (smbclient) → WebDAV. Walking a local filesystem tree takes milliseconds; WebDAV costs one PROPFIND per directory (~55 ms on a consumer NAS), so prefer a mount when you have one. Paths may be written POSIX-style, as a Windows drive, or as a UNC path (Z:\kb, \\nas\share\kb, /mnt/nas/kb) — all are normalised.

Samba/CIFS mounts need extra care (we hit all of these in production): a mount can be rw and still read-only for the account; a dropped session makes the whole tree raise ESTALE; and timestamps have one-second granularity. All three are handled explicitly — see docs/deployment.md.

3. Pull, index, search

dsh-kb doctor                       # check source reachability and mirror hygiene
dsh-kb refresh                      # sync + index — what a scheduler should call
dsh-kb search "mount stale" -n 5
dsh-kb search "UNIQ-000123" --json  # machine-readable
dsh-kb read z0-public/mount.md --start 1 --lines 40
dsh-kb push                         # upload local _incoming/ proposals

Only sync, push and doctor write to the share (and doctor writes just two probe files — see the CLI table). diff also reads the source listing (to report what changed remotely), but it never writes to the share, the mirror or the index — it is safe to call from a timer. The share can be offline, a certificate can expire, a laptop can be away — search still works. This is a hard requirement: otherwise "offline capable" is a lie.

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Privacy model

Zone	Mirrored locally	Indexed	Returned by default
z0-public	✅	✅	✅
z1-restricted	✅	✅	✅ (inject: false, for injectors to respect)
z2-sensitive	❌ never copied	❌	❌ (requires explicit --zone z2)
z3-skills	✅	❌ never indexed (once registered)	❌ (own entry point: dsh-kb skills; 2.3k SKILL.md would drown retrieval)
_meta/, _incoming/	✅	✅	✅ (layout, not private content)
paths matching never	❌	❌	❌

never defaults to credentials, .credentials, secrets, id_rsa, 密钥 ("keys"), 密钥库 and is extendable. All three enforcement points are covered by tests.

z3-skills is not built in. Register it in config.json with "index": false before moving files into it — an unregistered top-level directory is mirrored and indexed. See examples/config.json.

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CLI

Command	Writes?	Purpose
status	—	configuration, source, mirror and index state, freshness
doctor	✔ probes	per-check diagnostics, including "does the mirror contain anything it should not" (writes two probe files, see below)
diff [--incoming-days DAYS] [--json]	— (reads the source)	read-only health report: remote changes, local mirror gaps, index drift, backlog, conflicts, escapes
rag-check [--prune] [--force]	only with --prune	vector store ↔ index consistency, page and chunk level; report-only (dry-run) by default
sync [--prune] [--full] [--dry-run]	✔	incremental pull; --prune is opt-in; --full ignores the manifest
index	✔ (local)	rebuild the index
refresh [--prune] [--full]	✔	sync + index
search <q> [-n] [--zone] [--snippet] [--refresh] [--no-auto-refresh] [--json]	—	ranked search
read <path> [--start] [--lines] [--json]	—	line window
skills search|show|stats	—	query the skill library in the z3-skills asset zone
supersede-check [--json] [--proposal FILE]	—	supersession health (A1–A5) and the R1 proposal gate
import --from PATH [--format NAME] --prefix P [--zone Z] [--write]	only with --write	turn another tool's export into pages — docs/import.md
push [files…] [--force] [--expect SHA256] [--log [N]] [--allow-undeclared] [--json]	✔	upload proposals (the supersession gate can refuse the batch)
mcp	—	run the MCP server on stdio (for agent hosts)

Shared flags are accepted before or after the subcommand.

Not everything labelled "diagnostic" is read-only. doctor is the one exception: it creates and deletes a probe file in home, and — when the source exposes it — writes and deletes a probe under _incoming/ on the share, to answer "can push work at all?". The probes are removed again; nothing else is written. Don't wire doctor into anything that must be strictly read-only.

Supersession is a first-class relation. A page may carry supersedes: [...] or superseded_by: <path> in its front matter, and dsh-kb supersede-check reports the four ways that relation goes wrong: a link nobody answers (not reciprocal), a cycle, a dangling target, and a page claimed by two successors. The same relation is a gate on writes: push refuses a whole batch when a proposal's title collides with a live page and it declares neither supersedes: nor not_superseding: "<why>" — --allow-undeclared is the explicit bypass. One prompt at the write door is cheaper than untangling two near-duplicates that both look current six months later.

Exit codes. diff and rag-check share one contract: 0 clean, 10 something needs a human, 11 local state is unreadable / the store is broken. rag-check --force without --prune is a usage error and exits 1 — it would override nothing, so it refuses rather than pretending it forced something.

What the two reports actually compare

dsh-kb diff compares two pairs, in two directions, and splits both by file kind (markdown / other) so image or JSON churn can't drown the .md diff:

• remote listing vs manifest → content: what changed on the share. Report only, never an alarm — the next sync converges.
• manifest vs local mirror → mirror_gaps: what is wrong with this machine's copy. Covers every file, not just Markdown. A local file that the snapshot recorded and the disk lost is mirror-gap (exit 10) — it used to be invisible, because remote and manifest were both unchanged while sync would re-fetch it. Extra local files are counted but not alarmed: sync --prune cannot delete them, so a human has to decide.

dsh-kb rag-check compares the vector store against the index at page (path) and chunk (path + startline) granularity. The chunk view is what catches "one page, five chunks, only two embedded" — page-level counters stay at 0 for that shape. On an older store whose tables have no startline column the comparison degrades to page-level and says so in note.

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Configuration reference

Key	Env	Default	Meaning
source	DSH_KB_SOURCE	auto	auto / local / mount / smb / webdav
mount	DSH_KB_MOUNT	—	mount point (Samba/CIFS, NFS, or a plain directory)
local_root	DSH_KB_LOCAL_ROOT	—	plain directory
smb_share	DSH_KB_SMB_SHARE	—	unmounted Samba share, e.g. //host/share (needs smbclient)
webdav_root	DSH_KB_WEBDAV_ROOT	—	WebDAV collection URL
stale_minutes	DSH_KB_STALE_MINUTES	15	auto-refresh threshold
mtime_tolerance	DSH_KB_MTIME_TOLERANCE	1.0	mtime delta still counted as "unchanged"
snippet_chars	DSH_KB_SNIPPET_CHARS	240	snippet width
default_limit	DSH_KB_DEFAULT_LIMIT	8	hits per query
zones / never	—	see above	policy
rag.*	DSH_KB_RAG_*	all off	optional semantic arm — see docs/semantic-arm.md

WebDAV secrets are read only from the environment or a credentials file, never from config.json:

export DSH_KB_WEBDAV_USER=me
export DSH_KB_WEBDAV_PASSWORD=...            # or DSH_KB_WEBDAV_CREDENTIALS=<key=value file>
export DSH_KB_WEBDAV_CERT=... DSH_KB_WEBDAV_KEY=... DSH_KB_WEBDAV_CA=...   # mutual TLS
export DSH_KB_WEBDAV_CONNECT=192.0.2.10      # connect to an IP, verify TLS against the CN

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Search behaviour

• CJK: indexing keeps the original text and appends adjacent character bigrams, so 挂载 matches 重新挂载 (a 2-character query, which a plain unicode61 tokenizer cannot do).
• Latin and identifiers: the original text is indexed verbatim, so z2-sensitive, page-000123.md and fts5 all match.
• Two passes: strict AND first, then a relaxed OR restricted to selective tokens. Results carry relaxed: true and dropped: [...] so the caller knows precision was traded for recall.
• Snippet location prefers the whole query as a literal, then word-boundary token hits — so searching mount -a reports that line, not the line holding /proc/mounts.

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Measured performance

Single Linux host, bundled SQLite with FTS5, synthetic corpus (~3.4 KB per page, mixed Chinese and English):

Pages	Corpus	Index build	Index size	Blow-up	Engine (worst case)	Exact identifier	CLI end-to-end
1 000	3.3 MB	0.38 s	7.7 MB	2.3×	—	—	46 ms
5 000	16.7 MB	1.6 s	33.1 MB	2.0×	—	—	59 ms
10 000	33.4 MB	3.2 s	62.2 MB	1.9×	29.6 ms	0.2 ms	72 ms

• End-to-end includes ~30 ms of Python interpreter start-up.
• The spread is large: an exact identifier takes 0.2 ms, a generic keyword that matches nearly everything takes 29.6 ms.
• A search returns about 2.4 KB, independent of corpus size.

The index stays near 1.9× because the FTS5 table stores no content (content='') and the text lives once in a separate meta table, joined back by rowid. Inlining the text into FTS5 measured 8.1×.

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Agent integration

Positioning: a supplement, not standing context. Nothing is auto-injected and nothing is searched per turn — it is consulted once, when past decisions, environment facts or history are actually needed.

Two shapes, one implementation (dsh_kb.service), so their output cannot drift:

Shape	When	Cost
CLI + --json	any agent that can run a command	one subprocess and one tool round-trip (~30 ms)
MCP server (stdio)	the host speaks MCP (recommended)	~420 tokens standing (1559-char tools/list + 125-char instructions, measured), one less layer per call

dsh-kb mcp            # JSON-RPC on stdin/stdout, exposing search / read / status / skills

Wiring it into DSH is one insert (other hosts: docs/agent-integration.md):

- id: mcp-kb
  name: '@deepseek-ai/dsh-mcp-client'
  config:
    serverName: kb
    transport: stdio
    command: python3
    args: [-m, dsh_kb, mcp, --home, /srv/dsh-kb, --source, auto, --mount, /mnt/nas/kb]
    env: { PYTHONPATH: /opt/dsh-kb/src }

Why the configuration goes in args, not env: some hosts scrub the environment of the MCP child process (credential-shaped names, stale prefixes). Command-line arguments are never scrubbed. Measured: with every DSH_KB_* variable removed and only args set, it still works against a real share.

Design choices — one optional deep flag, no mode knobs, no write tools, failures returned as tool results — are in docs/design.md.

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Optional semantic arm (off by default)

Everything above is lexical. On top of it there is an opt-in layer that can add embedding hits, a cross-encoder, or a remote model — and it is deliberately outside the zero-dependency promise:

Layer	Needs	Default
Vector arm	numpy + a vector store + an embedding endpoint	off
Cross-encoder rerank	onnxruntime/tokenizers + ~280 MB model	off
ONNX embedding backend	~610 MB model (A/B experiment)	off
Deep rerank (deep=true)	an OpenAI-compatible endpoint + key	off

With the defaults, import dsh_kb never touches third-party code and a search is byte-identical to 0.1.0. Read docs/semantic-arm.md before enabling anything: it lists what each layer needs, where the models come from, and which of them send page text off the machine (deep does; that is the one to leave off if your bundle holds anything you would not paste into a chat window).

Importing from another tool

If the knowledge already exists somewhere else, import turns that export into pages instead of asking you to copy files by hand:

dsh-kb import --from ~/notes/obsidian --format obsidian --prefix notes        # dry run
dsh-kb import --from ~/notes/obsidian --format obsidian --prefix notes --write
dsh-kb refresh

Adapters for mem0, Obsidian, Notion's Markdown export, basic-memory, plain JSON/JSONL and dsh-kb's own records/v1. It is additive (never deletes), deterministic (a second run is a no-op), and refuses to write into z0-public without --allow-public. See docs/import.md.

Status

Alpha (0.3.0) with 370 tests (unit, end-to-end, transport, CLI smoke and MCP — all offline):

python3 -m unittest discover -s tests -t . -v

With the official MCP SDK installed, one extra interop test runs (a real client against this hand-rolled server); otherwise it skips.

Roadmap

• MCP server (mcp__kb__search / read / status / skills)
• Semantic fallback, used only when keyword search returns nothing — opt-in and off by default (docs/semantic-arm.md)
• Scheduled refresh (systemd timer / cron examples)
• Chinese embedding evaluation to pick a default model
• Optional read-only HTTP viewer

License

MIT — see LICENSE.