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sql-server-query-ai/kb/raw/README.md
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ahmed mohammedandClaude Opus 4.7 8b42a0931c docs: karpathy-pattern wiki built from the spec, plan, design contract and decisions ledger
kb/ is its own wiki root: 34 pages across 7 categories, index.md catalog, log.md, AGENTS.md schema layer, and raw/ holding copies of README, the spec, the 13-task plan, the design master and the SDD ledger.

Every page cross-references 3 to 6 others and all 107 wikilinks resolve except two [["list"]] artefacts inside JSON snippets in the copied plan.

Kept separate from the repository root on purpose: its generated graph lands in kb/.ua/ instead of overwriting the code graph.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-09-22 12:14:05 +03:00

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# SQL Server qAI
Paste a SQL Server ShowPlan XML (or drop a `.sqlplan`), get a report with several ranked remediation options and
rewritten queries. Two models, one job: a **generative analyst model** proposes the options and writes the report, and
**Jev** (TypeSafe System One) scores every option and decides which one to run first. Jev never writes SQL; it returns
typed scores, a choice, and confidences. The report renders as OpenUI elements, not a markdown blob.
## Pipeline
1. `lib/digest.ts` parses the XML in code: top operators, missing indexes, warnings, waits, estimate-vs-actual row
errors, tables, statement text, subtree cost, parallelism. Raw XML never reaches a model.
2. The analyst model receives the digest JSON and proposes 2–4 genuinely different options (index, rewrite, statistics,
parallelism, schema), each with ordered action steps and an optional rewrite.
3. Jev answers four questions per option (`bottleneck_fit`, `semantic_safety`, `ease` `score`; `root_cause` `noul`) and
one cross-option call (`first_to_run` `choice`, `anything_worth_running` `noul`).
4. `lib/jev.ts` combines them with visible weights (0.40 / 0.30 / 0.15 / 0.15 — four numbers in that file, the only
tuning knob) and prints a verdict. Jev's pick leads only when its confidence clears 0.5; otherwise the report says
the order came from composite scoring.
5. The analyst streams the final report as OpenUI Lang; `<Renderer>` paints it as it arrives.
Getting the XML: SSMS → **Ctrl+M** (include actual execution plan) → run the query → right-click the Execution Plan tab
→ *Save As .sqlplan*. No SSMS: prefix the batch with `SET STATISTICS XML ON;` and copy the XML result cell.
## Run it
This repo runs as a plain Node process. `Dockerfile` and `docker-compose.yml` are ready, but the image has **not
been built or run on this machine** (Docker is not installed here); treat it as untested until
`bash scripts/smoke.sh --docker` says otherwise. Local work is `npm run dev`.
```bash
npm install
cp .env.example .env.local # then fill in APP_SECRET and DATA_DIR
npm run dev # http://localhost:3000
bash scripts/smoke.sh # healthz, login page, signed-out redirect, analyze needs a session
```
`.env.local`:
| key | why |
| --- | --- |
| `APP_SECRET` | ≥16 chars. Encrypts API keys at rest (AES-256-GCM). Changing it makes stored keys unreadable. |
| `DATA_DIR` | where `qai.db` (SQLite, `node:sqlite`) lives. Default `./data`. Docker uses `/data`. |
| `COOKIE_SECURE` | set to `1` only when the app is served over https without a proxy that sets `X-Forwarded-Proto`. The session cookie is `Secure` when that header says `https`; forcing it on plain http makes Safari refuse to store it. |
Model keys are **not** config. Each user types them in the app (first-run guide or `/settings`), so a second user
brings their own endpoint and their own spend. Blank key field = keep the stored key.
- Analyst: any OpenAI-compatible base URL + key + model, plus optional extra JSON params.
A local endpoint such as `http://192.168.88.131:8888/v1` works; use key `EMPTY` when the server has no auth.
For a reasoning model (Qwen3 on vLLM) put `{"chat_template_kwargs":{"enable_thinking":false}}` in **Extra params**:
with thinking on, the model spends the whole context budget reasoning and the report arrives empty.
Both model calls default to `max_tokens: 2048`; the same field overrides it.
- Jev: a TypeSafe API key from `console.typesafe.ai/keys`, model usually `jev-latest`.
## Tests
```bash
npm test # node --test on tests/*.test.ts, no test framework installed
npm run build # production build + type check
npm run e2e # builds first, then Playwright against the production server
```
The unit suites run offline: the analyst and Jev are exercised through injected `fetch`, so no key and no network.
`npm run e2e` is the opposite — `pree2e` builds, then Playwright drives `npm run start` (the production server,
not `next dev`: cookie flags and hydration differ, and that difference once hid a sign-out bug) against the real
models. It reads `JEV_API_KEY` from `.env.local`. Expect it to be slow; that is the models, not the harness.
Projects: `chromium` (everything except the session spec, reusing one signed-in session), `webkit` (Safari —
session survival only, because it is the one engine that rejects a `Secure` cookie over `http://localhost`),
and `mobile` (Pixel 7, layout only, so no model run is paid for twice).
`tests/digest.test.ts` skips unless a genuine plan exists at `fixtures/real.sqlplan`. Drop one there from SSMS and the
skip becomes the real check on ShowPlan attribute spellings.
## Git remote (Gitea)
Origin points at the self-hosted Gitea:
```bash
git remote -v # http://192.168.88.39:3000/ahmed/sql-server-query-ai.git
git push origin feat/sql-server-qai
```
The remote URL deliberately contains **no password**. Git prompts for the username (`ahmed`) and password the first
time, or you can keep them out of prompts:
```bash
git config credential.helper osxkeychain # remember them locally
git push origin feat/sql-server-qai
```
Never commit the password into `.git/config`, a remote URL, or this README — a stored credential in the repo is
readable by whoever you hand the repo to. If you want it non-interactive in CI, create a Gitea token
(Settings → Applications) and use `http://192.168.88.39:3000/ahmed/sql-server-query-ai.git` with the token as password.
## Layout
```
lib/digest.ts XML → bounded digest (the only shape models see)
lib/analyst.ts propose 2–4 options; stream the final OpenUI report
lib/jev.ts questions, weights, ranking, verdict, fallback
lib/report-ui.tsx OpenUI component library — the same module builds the prompt and renders the answer
lib/db.ts node:sqlite: users, sessions, settings, analyses
lib/vault.ts AES-256-GCM seal/open/mask for credentials
lib/auth.ts scrypt passwords, hashed session tokens (DB stores the SHA-256, never the token)
app/api/analyze SSE pipeline: digesting → proposing → judging → writing
tests/ offline unit suites tests-e2e/ Playwright
docs/superpowers/ spec + the 13-task implementation plan this was built from
```
Design: dark terminal console, JetBrains Mono for data, IBM Plex Sans for prose, one phosphor accent.
Print stylesheet flips to paper white and drops the rail, buttons and chrome — that is the Export to PDF path.