lilixinsniper/dsh-intent-recognition

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A DeepSeek Harness (DSH) plugin: five-layer intent recognition pipeline + scenario/intent management UI + PostgreSQL/pgvector storage + distributable API services + DSH-native routing (agent tools / workflows / subagents / Cordis services).

User query
   │
   ▼
┌─ L1 rule hard filter ───────── greeting / exit / command / keyword strong match (lexicon + regex + black/white lists)
│      │ miss
├─ L2 vector retrieval ───────── pgvector cosine search compresses the intent library to Top5–Top10 candidates
│      │
├─ L3 Jev probabilistic scoring ─ high confidence routes directly → mid → L4; low → L5
│      │
├─ L4 fine-tuned model ───────── BERT / small LLM arbitrates near-duplicate intents and boundary cases
│      │
└─ L5 LLM structured fallback ── multi-intent / cross-turn context / slot extraction / open queries, JSON output

Every layer can be toggled off per service — disabled layers are skipped entirely.

Control plane + data plane

• Control plane (management UI): the Intent Recognition sidebar panel (same level as the Plugins entry; pin/unpin from the sidebar's More menu), plus a Settings → Intent Recognition section — both render the same management UI. Tooling only; it never joins the conversation.
• Data plane (consumable services): a running service can be consumed two ways:

Channel	Notes	Best for
Agent tool (recommended)	each running service registers intent_recognize_<service-slug>; high-confidence hits execute the route inside the tool	DSH main agent routing user turns
Cordis service	ctx.intentRecognition.predict(service, query, context) for other plugins	programmatic integration

Features

• Intent management: per-scenario intent catalog (name/code/description/examples/keywords/blacklist/whitelist/route config), Excel import (merge/replace) & export, one-click embedding rebuild.
• Service management: per-scenario API services, independent switches & parameters per layer, services can run / stop / delete / copy; per-service API keys; built-in test console and call logs.
• Vector lab: a dedicated tab for layer-2 recall debugging — recall tests (type a question, see per-chunk scores and the aggregated intent candidate set, optionally inheriting a service's threshold/TopK), chunk browsing (example text / dims / building model / timestamp, with single-chunk delete), and per-scenario coverage overview with in-place rebuild.
• Route execution: per-intent routes (none / DSH workflow / subagent / Cordis service) with a per-service route_mode (auto vs suggest).
• Model configuration: embedding, Jev, fine-tuned and LLM models are all page-configured (OpenAI-compatible or plain http JSON endpoints), with connectivity tests.
• Storage: PostgreSQL + pgvector (vectors and business data in one database).
• Agent integration: running services register/revoke intent_recognize_<slug> tools automatically; optionally contributes a system-prompt section listing available intent services.

Screenshots

Intent management	Service management

Vector lab	Model configuration

Test console (five-layer trace)	Agent tool call in a chat

Install

Prerequisites: Node.js ≥ 20, DSH installed (npm i -g @deepseek-ai/dsh).

# 1. Start PostgreSQL + pgvector (or reuse your own instance)
docker compose up -d

# 2. Install the plugin
dsh plugin --profile web add dsh-intent-recognition
# or from a local checkout / tarball
dsh plugin --profile web add ./dsh-intent-recognition
dsh plugin --profile web add ./dsh-intent-recognition-0.3.3.tgz

# 3. Restart `dsh web` (desktop: restart the app or reload the page, Ctrl+R)
#    —— client bundles load from the boot graph at page start; a reload is
#    required after install before the sidebar entry appears
# 4. Click "Intent Recognition" in the left sidebar (or Settings → Intent Recognition)
# 5. First run: open the "Database" tab, enter host/credentials, then
#    "Test" → "Test & Save" — the pool hot-swaps immediately, no restart

The desktop install flow from the official npm registry (Plugins → Add plugin → enter dsh-intent-recognition, source = npm official registry):

Add plugin	Installing	Installed (running)

When to configure the database: after install, on first open of the page — not at install time. The DSN cannot live in the database itself, and installing a plugin should not require a ready-made YAML plus a running instance. The page-saved DSN persists to a per-user config file (below); the patch databaseUrl is only the fallback. While the database is unreachable the plugin still loads, the sidebar panel still opens, data routes answer 503, and a banner on the page guides recovery.

Desktop note: if the sidebar entry is missing after install/upgrade, reload the page first (Ctrl+R or restart the app). The page loads the plugin client roster once at boot — the official plugin manager shows the same "refresh" prompt after installs. If the entry is still missing after a reload, check Settings → Intent Recognition: both come from the same client bundle, and if neither renders the client half never loaded — confirm the component shows "running" on the plugin detail page.

The sidebar entry registers through DSH's sidebar.panellist + main panel slots — the same mechanism as the built-in Plugins and Scheduled Tasks entries: clicking the icon opens the full management page in the main column, and the shell's More menu provides pin/unpin out of the box.

The package declares dsh.bundle.patch, so dsh plugin add mounts it into the profile bundle stack automatically — no manual cordis.patch.yml editing.

Database connection (page-managed)

The "Intent Recognition → Database" tab manages the connection end to end: a structured form (host/port/database/user/password/SSL) or a raw DSN, Test connection (also probes pgvector availability), Test & Save (persists + hot-swaps the pool without any restart), and Clear page config (fall back to the patch DSN).

While the database is unreachable, a red banner at the top of the panel guides you to the Database tab; saving hot-swaps the pool and takes effect immediately:

Disconnected banner	Database tab (connected)

• Priority: page-saved DSN > cordis.patch.yml databaseUrl > built-in default.
• Persistence: %APPDATA%/dsh-intent-recognition/connection.json (Windows), ~/Library/Application Support/dsh-intent-recognition/connection.json (macOS), ~/.config/dsh-intent-recognition/connection.json (Linux); override with the DSH_INTENT_RECOGNITION_CONFIG env var. File mode 0600; the password is masked as *** in API responses (submitting *** keeps the stored one).
• Fault tolerance: an unreachable database never blocks plugin load; every management route except /health and /connection* returns 503 with a recovery banner in the UI. Fix the connection in the Database tab and save to recover.
• pgvector: with autoMigrate on, a successful save runs CREATE EXTENSION IF NOT EXISTS vector plus schema migration automatically; the test button warns beforehand when the target lacks pgvector.
• Database maintenance (v0.6.0): below the connection form, Check / Create / Drop database buttons manage the DSN's target database. Create does everything in one shot — create the database, install pgvector, and initialize all ir_* tables. Drop is destructive: a red warning dialog requires typing the full database name to confirm, and system databases (postgres/template0/template1) are refused. Maintenance runs with the same credentials against the instance's postgres maintenance database, so it works even while the business database does not exist yet.

Plugin configuration

Field	Default	Notes
databaseUrl	postgres://postgres:postgres@127.0.0.1:5432/dsh_intent	Fallback DSN (page-saved config wins); pgvector must be available
embeddingDim	1024	Must match the configured embedding model; changing it requires an embedding rebuild
autoMigrate	true	Create tables/indexes on load (HNSW cosine index, pgvector ≥ 0.5)
adminToken	''	Bearer token protecting the management API; empty = DSH loopback only
registerTool	true	Register one intent_recognize_<slug> tool per running service
contributePrompt	true	Inject a system-prompt section listing available intent services

Intent routes

Each intent may carry a route — "where to go next" once matched:

Route type	Config	Behaviour
none	—	Return the recognition result only
workflow	target (run name) + workflow script	Starts a DSH workflow run; the script orchestrates freely (call subagents, tools, other plugins)
subagent	target = agent preset id	Spawns a subagent with that preset to handle the query (provider: spawn, result returned to caller)
service	target = serviceName.method	Calls a method of another Cordis service in the host (capability provided by another plugin)

Execution policy (route_mode)

• auto (default): deterministic hits (L1 / L3 / L4) execute the route inside the tool — the cheap deterministic path actually lands; L5 (LLM fallback) hits only return the route as a suggestion for the main agent.
• suggest: never executes; the route is returned to the caller (agent or HTTP client).

Results carry route when configured; after execution they also carry route_result (ok / value / error).

Workflow scripts

With route type workflow, write the script directly in the intent editor (a collapsible full guide is built into the page). The script receives:

Variable	Content
args.query	raw user input
args.intent	matched intent code
args.confidence	0–1
args.matched_layer	l1–l5
args.slots	slots extracted by L5
args.context	caller-provided context array
agent(prompt)	spawn a subagent inside the script
meta	run metadata (meta.name = route target)

const intent = args.intent;
if (intent === 'query_balance') {
  return await agent(`User wants their balance. Utterance: ${args.query}. Check and reply.`);
}
if (intent === 'transfer') {
  const amount = args.slots?.amount ?? 'unknown amount';
  return await agent(`User wants to transfer ${amount}. Utterance: ${args.query}. Confirm, then execute.`);
}
return `Unhandled intent: ${intent}`;

Runs are recorded with meta.name = <route target> (sanitized kebab-case) and are visible in DSH's run panel.

Plugging downstream capabilities into DSH

The whole system treats DSH as the backbone — intent routing and downstream agents/workflows all attach as plugins:

1. Workflows: scripts live in the intent's route config and travel with the catalog via Excel import/export ("workflow script" column). Reuse capabilities via agent() or route type service.
2. Subagents: ship downstream capabilities as agent preset plugins (see @deepseek-ai/dsh-agent-preset); put the preset id in the route target.
3. Cordis services: a downstream plugin does ctx.reflect.provide('xxxService', ...) in apply; set the route target to xxxService.methodName. The method is called as (result, exec) where result carries query/intent/confidence/slots.

Using services in the chat window (Agent tools)

Once a service is running, the plugin registers a chat tool intent_recognize_<slug> with DSH (slug derived from the service name; revoked automatically when the service stops). From then on, just ask in any conversation — the main agent calls the tool when relevant and the route config dispatches to workflows / subagents / Cordis services. You can also invoke it explicitly, e.g. "use intent_recognize_bank_router to check: I want to transfer money".

Each service card shows a "chat tool registered / not registered" badge; if registration fails (e.g. a name clash), the reason is shown under the badge. State syncs in real time with service start/stop.

Note: tool registration is controlled by the registerTool plugin option (on by default) and requires the DSH tools service (present in web/desktop builds).

Jev extra-config auto-generation (systemone wire format)

The Jev model dialog offers two extra-config generators; pick a scenario whose intents are configured, then choose:

• AI generation: calls a configured LLM (add one under model configuration first; if none is configured or the call fails, the error is surfaced — use the rule template instead). The full intent descriptions (definition / inclusion signals / exclusion rules / boundary distinctions, with examples) are passed to the model verbatim, and the prompt demands faithful preservation of those rules.
• Rule template: deterministic, no LLM required — embeds each intent's full description into instructions with generic five-band scoring criteria.

Both generate all three judgment modes at once (modes.score / modes.choice / modes.noul). Since v0.6.0 each mode carries a single question named intent_recognition that judges every candidate intent in one call:

Mode	type	criteria	answer	score mapping
score	score	array of per-intent decision descriptions, plus intent_order (intent codes aligned 1:1 with criteria)	score array (in intent_order order) or {code: score} object	array indexes map via intent_order
choice	choice	{intent name: decision description} object	one intent name	chosen = 1, others = 0
noul	noul	— (map binds “是”/“否” to the two intent codes)	“是”/“否”	per map: hit = 1, other = 0

noul is only generated for exactly-2-intent scenarios — yes/no cannot cover more; don't pick noul as the layer-3 mode otherwise.

Which mode runs is chosen per service: the service editor's layer 3 has a "judgment mode" dropdown (score/choice/noul, default score); layer 3 then sends the corresponding modes.<mode> question. Legacy extra configs (per-intent modes questions, or a single top-level questions) keep working — layer 3 falls back to per-intent calls.

Layer-3 thresholds decide the outcome: top score ≥ high threshold → direct hit (skips L4/L5); [mid, high) → compressed candidates go to L4 arbitration; below mid → L5 fallback. The result is a draft — review it in the extra-config field before saving.

A Jev model with this extra config (api_style=http) switches layer 3 to the systemone wire format:

POST {base_url}
{ "model": "<model>", "state": "<user input>", "questions": { "intent_recognition": <modes.<mode> question> } }
→ { "answers": { "intent_recognition": [0.9, 0.1] | "查询余额" | "是" } }

Score answers may be an array aligned with intent_order, a {scores|values|results: [...]} wrapper, or a {code: score} object; choice answers are an intent name (「」 quotes, codes, case and containment tolerant); noul answers are “是”/“否” mapped via the question's map. All three modes also accept object answers like {choice: "name", confidence: 1, probabilities: {name: p}} — probabilities maps are scored directly, otherwise the choice/answer/value label field is matched. Legacy per-intent answers {"answers": {"<intent code>": ...}} keep working: numbers, booleans, "是"/"否"/"true" strings, or {score|probability|confidence} objects.

Layer-2 retrieval transparency

The layer-2 "confidence threshold (min similarity)" (minScore) in the service editor is the slice filter — recalled chunks below it never become candidates. Test traces now expose it: layer 2 carries minScore / topK / chunks (every raw recalled slice with its score and pass/fail) / candidates (post-aggregation), so "recalled but not adopted" is directly attributable to threshold filtering vs aggregation truncation; layer 3 traces carry the judgment mode.

Vector lab (layer-2 debugging)

The Vector Lab tab answers "why was recall wrong for this question":

1. Recall test: pick a scenario, type any question, and the plugin replays the engine's real layer-2 logic (same searchSimilar, per-intent best chunk, minScore filter, TopK truncation). Optionally reference a service to inherit its embedding model and layer-2 threshold/TopK, or pick a model and tweak the threshold ad hoc. Two result tables: the intent aggregation (what layer 2 would actually emit downstream) and the raw chunk hits (every recalled slice with its score).
2. Chunk browser: all embedding slices of a scenario (example text, owning intent, dims, building model, build time), filterable by intent, paginated, with per-chunk delete for stale slices.
3. Coverage overview: per-scenario intent/example/chunk counts, coverage gaps, the models that built the slices, last rebuild time, and in-place rebuild with a chosen model.

Slices carry model provenance (model_id); after switching embedding models or editing examples, rebuild — otherwise old and new slices live in incompatible vector spaces.

Programmatic access

From another dsh plugin's apply(ctx):

const ir = ctx.get('intentRecognition');
if (ir) {
  const result = await ir.predict('bank-router', 'check my balance', []);
}

Excel template

Header row: Name | Code | Description | Examples | Keywords | Blacklist | Whitelist | Route Type | Route Target | Workflow Script. Multi-values inside examples are newline-separated, others use 、; route type is none / workflow / subagent / service (a target or script without a type infers workflow). The exported file is the template.

Development

pnpm install
pnpm typecheck
pnpm build                     # emits lib/index.js + lib/client.js (esbuild)
node test/smoke.mjs            # end-to-end pipeline (needs local PostgreSQL+pgvector, see test/README.md)
node test/cordis-load.mjs      # real Cordis host integration (tool registration / route dispatch / prompt)
node test/connection.mjs       # offline boot + 503 guard with an unreachable database (no DB needed)
node test/client-contract.mjs  # client bundle slot-registration contract (no browser needed)

Source layout: src/index.ts (host entry / Cordis integration), src/pipeline/engine.ts (five-layer engine), src/dispatcher.ts (route dispatch: workflow/subagent/service), src/service-tools.ts (per-service tool registration), src/routes.ts (REST API), src/db.ts (PostgreSQL/pgvector, hot-swappable pool), src/connection.ts (page-managed connection), src/models.ts (model calls), src/excel.ts (import/export), src/client/ (React management UI, settings.section + main + sidebar.panellist slots).

License

MIT