Version Platform License Core Health Coverage
Your support team's second brain — ML-powered answers from your own knowledge base, in under 25ms, without sending a single query to the cloud.
AssistSupport combines local LLM inference with a hybrid ML search pipeline to generate accurate, KB-informed IT support responses. A logistic regression intent classifier routes queries before a TF-IDF retriever finds candidates, and a cross-encoder reranker sharpens relevance before the response is drafted. The entire pipeline — app, sidecar, and model inference — runs on your machine. Core SQLite data and token material are encrypted at rest via SQLCipher (AES-256); optional vector-search embeddings stay local but are not currently encrypted at rest when vector search is enabled. See docs/SECURITY.md for the full security architecture.
User asks: "Can I use a flash drive?"
ML Intent: POLICY detected (86% confidence)
Search finds: USB/removable media policy in 21ms
Reranker: Cross-encoder confirms top result relevance
AI drafts: "Per IT Security Policy 4.2..."
You copy: Paste into Jira — done in under a minute
- ML intent classification — Logistic regression routes queries before retrieval starts.
- Sub-25ms hybrid search — TF-IDF retrieval plus cross-encoder reranking across 3,500+ KB articles.
- Encrypted local workspace — Core SQLite data and token material stay local and encrypted at rest via SQLCipher (AES-256). Vector-search embeddings are local but plaintext at rest when the optional vector store is enabled.
- Trust-gated responses — Confidence modes and source grounding reduce unsupported output.
- Self-improving feedback loop — KB gap analysis turns low-confidence patterns into follow-up work.
- Ops-ready workspace — Deployment, rollback, eval, triage, and runbook tooling ship with the app.
- Node.js 20+
- pnpm 9+
- Rust toolchain (stable) with Tauri v2 prerequisites for macOS
git clone https://github.com/saagpatel/AssistSupport.git
cd AssistSupport
pnpm installpnpm dev
pnpm tauri build
The public demo package is sanitized and uses only fictional Northstar Labs support data. Start with the sanitized demo plan for the fake-KB script, cleanup list, and verification checklist.
For portfolio review, use docs/portfolio/README.md as the single entry point. It links the screenshot set, one-pager PDF, deck PDF preview, case study, rehearsal kit, and 90-second video script. The current pre-publish handoff lives in docs/demo/portfolio-handoff-bundle.md.
Use the daily truth source for normal development and PR confidence:
pnpm health:repo
Use the release-only health command when you need heavier validation:
pnpm health:release
pnpm health:release runs the core repo health path plus coverage generation, build-time, bundle, asset, memory, and Lighthouse checks. API latency and DB query health are skipped unless BASE_URL and DATABASE_URL are configured.
Diff coverage is enforced in CI. Overall line coverage is informational and is not the primary health target.
The current health contract lives in docs/status/current-health.md.
# Static checks pnpm lint pnpm typecheck pnpm stylelint # Test suites pnpm test pnpm search-api:test pnpm test:ci pnpm ui:gate:regression # Release-only checks pnpm test:coverage pnpm perf:build pnpm perf:bundle pnpm perf:assets pnpm perf:memory pnpm perf:lhci
Create work on a compliant branch:
pnpm git:branch:create "your feature" featBefore opening a PR, run:
pnpm health:repo
Push your codex/<type>/<slug> branch and open a PR against master.
| Layer | Technology |
|---|---|
| Desktop shell | Tauri 2 + Rust |
| Frontend | React + TypeScript + Vite |
| ML search | TF-IDF, Logistic Regression, ms-marco-MiniLM-L-6-v2 |
| Local storage | SQLite (encrypted) |
| LLM inference | Local via llama.cpp (optional) |
| Fonts | IBM Plex Sans, JetBrains Mono |
AssistSupport is a Tauri 2 desktop app with a Rust backend handling search, encryption, and LLM orchestration. The ML pipeline runs as a local sidecar: intent classification happens first, then candidate retrieval via TF-IDF, then cross-encoder reranking to select the most relevant KB articles before response generation. Ratings feed back into the local SQLite store and surface gap analysis via the Ops workspace.
MIT