From 5f4c1a16acd272338ce620bc034acba2d81ed665 Mon Sep 17 00:00:00 2001 From: Guy Tonye Date: Thu, 11 Jun 2026 01:47:25 -0400 Subject: [PATCH] feat: add Maine DOC Insight desktop analytics tool Initial implementation of a Tauri + React dashboard for Maine DOC education program data, including six data views, an AI chat panel with multi-provider support, and automated data quality assessment. Co-Authored-By: Claude Sonnet 4.6 --- .gitignore | 5 + README.md | 215 +- eslint.config.js | 36 + index.html | 12 + package-lock.json | 5280 ++++++++++++++++++++++++ package.json | 35 + src-tauri/Cargo.lock | 4228 +++++++++++++++++++ src-tauri/Cargo.toml | 21 + src-tauri/build.rs | 3 + src-tauri/icons/128x128.png | Bin 0 -> 9465 bytes src-tauri/icons/128x128@2x.png | Bin 0 -> 18302 bytes src-tauri/icons/32x32.png | Bin 0 -> 1955 bytes src-tauri/icons/Square107x107Logo.png | Bin 0 -> 7943 bytes src-tauri/icons/Square142x142Logo.png | Bin 0 -> 10585 bytes src-tauri/icons/Square150x150Logo.png | Bin 0 -> 11099 bytes src-tauri/icons/Square284x284Logo.png | Bin 0 -> 21309 bytes src-tauri/icons/Square30x30Logo.png | Bin 0 -> 1844 bytes src-tauri/icons/Square310x310Logo.png | Bin 0 -> 23013 bytes src-tauri/icons/Square44x44Logo.png | Bin 0 -> 2918 bytes src-tauri/icons/Square71x71Logo.png | Bin 0 -> 5330 bytes src-tauri/icons/Square89x89Logo.png | Bin 0 -> 6666 bytes src-tauri/icons/StoreLogo.png | Bin 0 -> 3507 bytes src-tauri/icons/app-icon.png | Bin 0 -> 11600 bytes src-tauri/icons/icon.icns | Bin 0 -> 135932 bytes src-tauri/icons/icon.ico | Bin 0 -> 32896 bytes src-tauri/icons/icon.png | Bin 0 -> 32585 bytes src-tauri/src/main.rs | 8 + src-tauri/tauri.conf.json | 47 + src/App.tsx | 282 ++ src/app.css | 410 ++ src/chat/actionItems.ts | 117 + src/chat/aggregator.ts | 153 + src/chat/contextPacket.ts | 81 + src/chat/fetchWithRetry.ts | 27 + src/chat/flatTables.ts | 89 + src/chat/prompts.ts | 360 ++ src/chat/providers.ts | 47 + src/chat/refusal.ts | 29 + src/chat/router.ts | 280 ++ src/chat/writer.ts | 209 + src/components/ChartFootnote.tsx | 31 + src/components/DataQualityBanner.tsx | 53 + src/components/DataUnavailableCard.tsx | 59 + src/components/FileLoader.tsx | 632 +++ src/components/OmsCards.tsx | 620 +++ src/components/SmartActionItem.tsx | 73 + src/components/chat/ChatDrawer.tsx | 578 +++ src/config/analytics.json | 41 + src/config/chat.json | 5 + src/config/data_quality.json | 30 + src/config/facilities.json | 12 + src/config/llm.json | 30 + src/config/models.json | 22 + src/config/thresholds.json | 60 + src/contexts/DataQualityContext.tsx | 11 + src/data/demoResponses.ts | 219 + src/data/mockData.ts | 697 ++++ src/data/schemaRegistry.ts | 309 ++ src/lib/analytics.ts | 1881 +++++++++ src/lib/dataQuality.ts | 342 ++ src/lib/demoExport.ts | 187 + src/lib/ingest.ts | 754 ++++ src/main.tsx | 9 + src/types.ts | 945 +++++ src/views/ComparisonView.tsx | 300 ++ src/views/Dashboard.tsx | 442 ++ src/views/DataQualityView.tsx | 799 ++++ src/views/FacilitiesView.tsx | 320 ++ src/views/HomeView.tsx | 1244 ++++++ src/views/InsightsView.tsx | 1385 +++++++ src/views/ProgramsView.tsx | 548 +++ src/views/ResidentsView.tsx | 534 +++ tsconfig.json | 21 + tsconfig.node.json | 10 + vite.config.ts | 19 + 75 files changed, 25195 insertions(+), 1 deletion(-) create mode 100644 .gitignore create mode 100644 eslint.config.js create mode 100644 index.html create mode 100644 package-lock.json create mode 100644 package.json create mode 100644 src-tauri/Cargo.lock create mode 100644 src-tauri/Cargo.toml create mode 100644 src-tauri/build.rs create mode 100644 src-tauri/icons/128x128.png create mode 100644 src-tauri/icons/128x128@2x.png create mode 100644 src-tauri/icons/32x32.png create mode 100644 src-tauri/icons/Square107x107Logo.png create mode 100644 src-tauri/icons/Square142x142Logo.png create mode 100644 src-tauri/icons/Square150x150Logo.png create mode 100644 src-tauri/icons/Square284x284Logo.png create mode 100644 src-tauri/icons/Square30x30Logo.png create mode 100644 src-tauri/icons/Square310x310Logo.png create mode 100644 src-tauri/icons/Square44x44Logo.png create mode 100644 src-tauri/icons/Square71x71Logo.png create mode 100644 src-tauri/icons/Square89x89Logo.png create mode 100644 src-tauri/icons/StoreLogo.png create mode 100644 src-tauri/icons/app-icon.png create mode 100644 src-tauri/icons/icon.icns create mode 100644 src-tauri/icons/icon.ico create mode 100644 src-tauri/icons/icon.png create mode 100644 src-tauri/src/main.rs create mode 100644 src-tauri/tauri.conf.json create mode 100644 src/App.tsx create mode 100644 src/app.css create mode 100644 src/chat/actionItems.ts create mode 100644 src/chat/aggregator.ts create mode 100644 src/chat/contextPacket.ts create mode 100644 src/chat/fetchWithRetry.ts create mode 100644 src/chat/flatTables.ts create mode 100644 src/chat/prompts.ts create mode 100644 src/chat/providers.ts create mode 100644 src/chat/refusal.ts create mode 100644 src/chat/router.ts create mode 100644 src/chat/writer.ts create mode 100644 src/components/ChartFootnote.tsx create mode 100644 src/components/DataQualityBanner.tsx create mode 100644 src/components/DataUnavailableCard.tsx create mode 100644 src/components/FileLoader.tsx create mode 100644 src/components/OmsCards.tsx create mode 100644 src/components/SmartActionItem.tsx create mode 100644 src/components/chat/ChatDrawer.tsx create mode 100644 src/config/analytics.json create mode 100644 src/config/chat.json create mode 100644 src/config/data_quality.json create mode 100644 src/config/facilities.json create mode 100644 src/config/llm.json create mode 100644 src/config/models.json create mode 100644 src/config/thresholds.json create mode 100644 src/contexts/DataQualityContext.tsx create mode 100644 src/data/demoResponses.ts create mode 100644 src/data/mockData.ts create mode 100644 src/data/schemaRegistry.ts create mode 100644 src/lib/analytics.ts create mode 100644 src/lib/dataQuality.ts create mode 100644 src/lib/demoExport.ts create mode 100644 src/lib/ingest.ts create mode 100644 src/main.tsx create mode 100644 src/types.ts create mode 100644 src/views/ComparisonView.tsx create mode 100644 src/views/Dashboard.tsx create mode 100644 src/views/DataQualityView.tsx create mode 100644 src/views/FacilitiesView.tsx create mode 100644 src/views/HomeView.tsx create mode 100644 src/views/InsightsView.tsx create mode 100644 src/views/ProgramsView.tsx create mode 100644 src/views/ResidentsView.tsx create mode 100644 tsconfig.json create mode 100644 tsconfig.node.json create mode 100644 vite.config.ts diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3b6ab90 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +node_modules/ +dist/ +src-tauri/target/ +.DS_Store +*.local diff --git a/README.md b/README.md index 224b3bf..6a3cc10 100644 --- a/README.md +++ b/README.md @@ -1 +1,214 @@ -# InsightsTool +# Maine DOC Insight + +A desktop analytics tool for Maine DOC education program data. +Built with [Tauri](https://tauri.app/) (Rust backend) + React + Chart.js. + +## What it does + +**Dashboard views** — six data views for exploring program and resident outcomes: + +| View | What it covers | +|------|----------------| +| Home | System-wide KPIs, top programs by completion, near-release engagement snapshot | +| Programs | Completion rates, waitlists vs. throughput, second-enrollment rates, breakdown by LSI/custody/education level | +| Residents | Never-engaged roster, near-release residents by engagement status, no-diploma residents not in education | +| Facilities | Facility completion rates (raw and mix-adjusted), operational focus areas | +| Comparison | Side-by-side facility comparisons | +| Data Quality | Field-level missing data report, metric quality status, integrity checks | + +**AI chat panel** — ask natural-language questions about the loaded data. The planner routes your question to the right metrics; the writer synthesizes a cited response. Supports multiple AI providers (see below). No resident names or DOC numbers are sent to any external API — only aggregated metrics. + +**Data quality assessment** — automatic checks on every loaded dataset. Missing files, low coverage, and suspicious values surface as per-view badges and a dedicated Data Quality view with field-level detail. + +**Demo mode** — explore sample AI responses without loading real data or entering an API key. Toggle the switch in the chat panel header. + +## Prerequisites + +- [Node.js](https://nodejs.org/) ≥ 18 +- [Rust](https://www.rust-lang.org/tools/install) + Cargo (for Tauri) +- [Tauri prerequisites](https://tauri.app/v1/guides/getting-started/prerequisites) for your OS + +## Quick start + +```bash +npm install + +# Run in dev mode (opens a hot-reload window) +npm run tauri dev + +# Or run just the web frontend (no Rust required) +npm run dev +# then open http://localhost:5173 +``` + +## Build for distribution + +```bash +npm run tauri build +# Output: src-tauri/target/release/bundle/ +``` + +## AI providers + +Open the chat panel (✦ Chat tab on the right) and select a provider. + +| Provider | Setup | Privacy | +|----------|-------|---------| +| Google Gemini | API key from [aistudio.google.com](https://aistudio.google.com) | Aggregated metrics sent to Google | +| OpenAI | API key from [platform.openai.com](https://platform.openai.com/api-keys) | Aggregated metrics sent to OpenAI | +| Anthropic Claude | API key from [console.anthropic.com](https://console.anthropic.com) | Aggregated metrics sent to Anthropic | +| Ollama (local) | See below — no key required | Nothing leaves your machine | + +### Local AI with Ollama + +No API key required. Runs entirely on your machine. + +**1. Install Ollama** + +Download from [ollama.ai](https://ollama.ai) and follow the installer for your OS. + +**2. Start the server** + +```bash +ollama serve +``` + +Ollama must be running before you use the chat panel. If you see a connection error, run this command first. + +**3. Pull a model** + +```bash +# Recommended — works on most laptops (~4.7 GB) +ollama pull qwen2.5:7b + +# Better output quality if you have a GPU or more RAM (~9 GB) +ollama pull qwen2.5:14b +``` + +**4. Enable in the app** + +Open the chat panel, select **Ollama (local)**, and click **Enable local AI**. + +**Notes:** +- The first response after pulling a model may be slow (~30s) while it loads into memory. Subsequent responses are faster. +- Structured query planning requires solid instruction-following ability. `qwen2.5:7b` is the minimum recommended size; `qwen2.5:14b` is more reliable for complex questions. +- If the model returns malformed output, the app retries once with a corrected prompt before surfacing an error suggesting a larger model. +- If Ollama is running on a non-default port or host, edit `src/config/llm.json` and change the `endpoints.ollama` value. + +## File schema + +All files are optional — insights are generated from whatever is loaded. Files are organized into three sections in the loader. + +### DOC data + +Exported from the Maine DOC / OMS system. Accepts CSV or Excel (`.xlsx`). + +**Core:** + +| Dataset | Key columns | +|---------|-------------| +| Resident Roster | `ID`, `STATUS_DESC`, `LOCATION_TO`, `LOC_TYPE_TO`, `ODARA_SCORE`, `STATIC99R_SCORE`, `LSI_RATING`, `HIGHEST_ED_LVL`, `CUSTODY_LVL`, `Earliest Release Date`, `SENTENCE_OFFENSE`, `Offense Date`, `COUNTY`, `LAST_PROGRAM_COMPLETED`, `JOB_ASSIGN` | +| Program Participation | `ID`, `GENDER`, `Facility`, `CUSTODY LEVEL`, `Housing Unit`, `Housing Pod`, `Room`, `Bed`, `Program`, `Program Status`, `Program Termination Or Completion Date`, `Earliest Release Date` | + +**Advanced** (toggle "Advanced data" in the loader to reveal): + +> **Note:** Advanced dataset column schemas are based on expected data structure and have not been cross-referenced against actual Maine DOC exports. Column names may differ from real exports. + +| Dataset | Key columns | +|---------|-------------| +| Incident Records | `ID`, `Incident Date`, `Incident Type`, `Severity`, `Sanction`, `Facility` | +| Work Assignments | `ID`, `Assignment Type`, `Role Title`, `Start Date`, `End Date`, `Transferable Skills Flag`, `Facility` | +| Case Plans | `ID`, `State ID Obtained`, `State ID Date`, `Job Lined Up`, `Housing Plan`, `Trust Account Balance`, `Savings Goal Met` | +| Credentials | `ID`, `Credential Type`, `Credential Name`, `Date Earned`, `Issuing Body`, `Verified` | +| Housing History | `ID`, `Move Date`, `Housing Unit`, `Pod`, `Room`, `Bed`, `Custody Level`, `Move Reason`, `Facility` | + +### UnlockEd platform data + +> **Note:** This is a subset of the data available from the UnlockEd platform, converted to CSV files for local use. + +CSV exports from the UnlockEd database. Drop multiple files at once onto the UnlockEd section. + +`users.csv`, `facilities.csv`, `programs.csv`, `program_classes.csv`, `program_class_enrollments.csv`, `program_completions.csv`, `program_class_events.csv`, `program_class_event_attendance.csv`, `user_session_tracking.csv`, `program_credit_types.csv` + +### Mapping & reference + +Optional. Shown when "Advanced data" is enabled. + +| File | Purpose | +|------|---------| +| `program_crosswalk.csv` | Maps DOC program names to UnlockEd program IDs; enables fuzzy-match fallback | +| `field_mapping.csv` | Maps OMS/DOC column names to internal tool fields; handles future export schema changes | + +## Project structure + +``` +maine-doc-insight/ +├── src/ +│ ├── chat/ +│ │ ├── providers.ts # Provider registry (Gemini, OpenAI, Claude, Ollama) +│ │ ├── router.ts # Planner — routes questions to metric queries +│ │ ├── writer.ts # Writer — synthesizes cited responses +│ │ ├── prompts.ts # System instructions and user turn builders +│ │ ├── aggregator.ts # Executes DataPlan against loaded data +│ │ ├── contextPacket.ts # Assembles metric context for the writer +│ │ ├── flatTables.ts # Flat table projections for the aggregator +│ │ ├── actionItems.ts # Action item extraction +│ │ ├── refusal.ts # Refusal message builder +│ │ └── fetchWithRetry.ts # Fetch wrapper with retry + network error handling +│ ├── lib/ +│ │ ├── ingest.ts # CSV/Excel parsing, buildCoreFromDOC, buildCoreFromUL +│ │ ├── analytics.ts # All metric computations +│ │ ├── dataQuality.ts # Data quality checks and grading +│ │ └── demoExport.ts # Exports synthetic demo data as downloadable CSVs +│ ├── data/ +│ │ ├── mockData.ts # Synthetic demo data generator +│ │ ├── schemaRegistry.ts # Metric registry (IDs, labels, table mappings) +│ │ └── demoResponses.ts # Canned AI responses for demo mode +│ ├── config/ +│ │ ├── chat.json # Chat panel UI settings (confidence threshold, drawer width) +│ │ ├── llm.json # Provider endpoints and generation parameters +│ │ ├── models.json # Default model lists per provider +│ │ ├── data_quality.json # Missing-data warn/crit thresholds and LSI clustering parameters +│ │ ├── analytics.json # Algorithm parameters: top-N, mix-adjustment, cohort windows, statistical constants +│ │ ├── thresholds.json # Display thresholds: attention flag scoring, QoL green/yellow bands, color cutoffs +│ │ └── facilities.json # Facility code → display name pairs (10 Maine DOC facilities) +│ ├── components/ +│ │ ├── chat/ +│ │ │ └── ChatDrawer.tsx # AI chat panel with provider selection +│ │ ├── FileLoader.tsx # Drag-and-drop CSV ingestion UI +│ │ ├── OmsCards.tsx # OMS metric cards +│ │ ├── ChartFootnote.tsx # Per-chart data quality footnotes +│ │ ├── DataUnavailableCard.tsx # Placeholder when required data is missing +│ │ ├── DataQualityBanner.tsx +│ │ └── SmartActionItem.tsx +│ ├── contexts/ +│ │ └── DataQualityContext.tsx +│ ├── views/ +│ │ ├── HomeView.tsx # Dashboard — KPIs, top programs, near-release +│ │ ├── ProgramsView.tsx # Completion rates, waitlists, second-enrollment, breakdowns +│ │ ├── ResidentsView.tsx # Near-release, never-engaged, no-diploma roster +│ │ ├── FacilitiesView.tsx # Facility completion rates (raw + mix-adjusted) +│ │ ├── ComparisonView.tsx # Side-by-side facility comparisons +│ │ └── DataQualityView.tsx # Field-level data quality report +│ ├── types.ts # All TypeScript types +│ ├── App.tsx # Shell + navigation + quality indicators +│ └── app.css # Dark analytics theme +├── src-tauri/ # Rust/Tauri backend (minimal) +├── index.html +├── vite.config.ts +└── package.json +``` + +## Notes on mix-adjustment + +The mix-adjusted completion rate applies a correction for LSI risk composition. +If a facility has a higher share of High/Maximum LSI residents than the system average, +its raw rate is adjusted upward proportionally — surfacing true operational performance +independent of resident population composition. + +Adjustment formula: +``` +mixAdjustedRate = rawRate + (facilityHighRiskShare - systemHighRiskShare) × 15pp +``` +This is a simplified first-order adjustment. A regression-based approach would be more +rigorous for formal reporting. diff --git a/eslint.config.js b/eslint.config.js new file mode 100644 index 0000000..1e9409f --- /dev/null +++ b/eslint.config.js @@ -0,0 +1,36 @@ +import tsPlugin from "@typescript-eslint/eslint-plugin"; +import tsParser from "@typescript-eslint/parser"; +import reactPlugin from "eslint-plugin-react"; +import reactHooksPlugin from "eslint-plugin-react-hooks"; + +export default [ + { + ignores: ["dist/**", "src-tauri/**", "node_modules/**"], + }, + { + files: ["**/*.{ts,tsx}"], + languageOptions: { + parser: tsParser, + parserOptions: { + ecmaVersion: "latest", + sourceType: "module", + ecmaFeatures: { jsx: true }, + }, + }, + plugins: { + "@typescript-eslint": tsPlugin, + react: reactPlugin, + "react-hooks": reactHooksPlugin, + }, + rules: { + ...tsPlugin.configs.recommended.rules, + ...reactHooksPlugin.configs.recommended.rules, + "@typescript-eslint/no-unused-vars": ["error", { varsIgnorePattern: "^_", argsIgnorePattern: "^_", destructuredArrayIgnorePattern: "^_" }], + "react/jsx-uses-react": "off", + "react/react-in-jsx-scope": "off", + }, + settings: { + react: { version: "detect" }, + }, + }, +]; diff --git a/index.html b/index.html new file mode 100644 index 0000000..cc9b551 --- /dev/null +++ b/index.html @@ -0,0 +1,12 @@ + + + + + + Maine DOC Insight + + +
+ + + diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..077da07 --- /dev/null +++ b/package-lock.json @@ -0,0 +1,5280 @@ +{ + "name": "maine-doc-insight", + "version": "0.6.1", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "maine-doc-insight", + "version": "0.6.1", + "dependencies": { + "@tauri-apps/api": "^1.5.0", + "chart.js": "^4.4.1", + "papaparse": "^5.4.1", + "react": "^18.2.0", + "react-chartjs-2": "^5.2.0", + "react-dom": "^18.2.0", + "xlsx": "^0.18.5" + }, + "devDependencies": { + "@tauri-apps/cli": "^1.5.0", + "@types/papaparse": "^5.3.14", + "@types/react": "^18.2.0", + "@types/react-dom": "^18.2.0", + "@typescript-eslint/eslint-plugin": "^8.60.1", + "@typescript-eslint/parser": "^8.60.1", + "@vitejs/plugin-react": "^4.0.0", + "eslint": "^9.39.4", + "eslint-plugin-react": "^7.37.5", + "eslint-plugin-react-hooks": "^7.1.1", + "typescript": "^5.0.0", + "vite": "^4.4.0" + } + }, + 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+ {/* ── Top bar ─────────────────────────────────────────────────────── */} +
+
+ Maine DOC · Insight +
+ {isDemo && ( + + DEMO DATA + + )} +
+ + {data && ( + <> + + {data.residents.length} residents · {data.facilities.length} facilities · {data.enrollments.length} enrollments + + + + )} +
+
+ + {/* ── Sidebar ─────────────────────────────────────────────────────── */} + + + {/* ── Main content ────────────────────────────────────────────────── */} +
+ {loading && ( +
+
+ Processing data… + +
+ )} + + {!loading && error && ( +
+ {error} +
+ )} + + {!loading && view === "load" && ( + loadDemo(scenario)} /> + )} + + {!loading && data && ( + + {view === "dashboard" && } + {view === "quality" && } + {view === "programs" && } + {view === "residents" && } + {view === "facilities" && } + {view === "comparison" && } + + )} +
+ + setPendingPrompt(null)} + onOpenChange={setChatOpen} + chatDemoMode={chatDemoMode} + onChatDemoModeChange={setChatDemoMode} + /> + + {/* ── Footer ──────────────────────────────────────────────────────── */} + {snapshot && ( +
+ snapshot:{snapshot.id.slice(0, 8)} + as-of:{snapshot.as_of_date} + schema:{snapshot.schema_version} + {isDemo && DEMO} +
+ )} +
+ ); +} diff --git a/src/app.css b/src/app.css new file mode 100644 index 0000000..912fc1d --- /dev/null +++ b/src/app.css @@ -0,0 +1,410 @@ +@import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500&family=IBM+Plex+Sans:wght@300;400;500;600&display=swap'); + +*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; } + +:root { + --bg: #0d0f12; + --bg2: #141720; + --bg3: #1c2030; + --bg4: #232840; + --border: rgba(255,255,255,0.08); + --border2: rgba(255,255,255,0.14); + --text: #e8eaf0; + --text2: #9ca3b0; + --text3: #606878; + --accent: #4a9eff; + --accent2: #2d7de0; + --green: #3db87a; + --amber: #f59e0b; + --red: #e05252; + --purple: #8b5cf6; + --teal: #14b8a6; + --font-sans: 'IBM Plex Sans', system-ui, sans-serif; + --font-mono: 'IBM Plex Mono', monospace; + --radius-sm: 4px; + --radius-md: 8px; + --radius-lg: 12px; + --row-sep: rgba(255,255,255,0.04); +} + +[data-theme="light"] { + --bg: #f5f6f8; + --bg2: #ffffff; + --bg3: #eef0f4; + --bg4: #e2e5eb; + --border: rgba(0,0,0,0.08); + --border2: rgba(0,0,0,0.16); + --text: #111318; + --text2: #3d4455; + --text3: #7a8394; + --accent: #2563eb; + --accent2: #1d4ed8; + --row-sep: rgba(0,0,0,0.05); +} + +html, body, #root { height: 100%; } +body { + background: var(--bg); + color: var(--text); + font-family: var(--font-sans); + font-size: 14px; + line-height: 1.6; + -webkit-font-smoothing: antialiased; +} + +/* ── Layout ──────────────────────────────────────────────────────────────── */ +.app-shell { + display: grid; + grid-template-columns: 220px 1fr; + grid-template-rows: 48px 1fr 28px; + height: 100vh; + overflow: hidden; +} + +.app-topbar { + grid-column: 1 / -1; + display: flex; + align-items: center; + gap: 12px; + padding: 0 20px; + background: var(--bg2); + border-bottom: 1px solid var(--border); + flex-shrink: 0; +} + +.app-topbar .logo { + font-family: var(--font-mono); + font-size: 13px; + font-weight: 500; + color: var(--accent); + letter-spacing: 0.06em; + text-transform: uppercase; +} +.app-topbar .logo span { color: var(--text3); } + +.app-topbar .topbar-right { + margin-left: auto; + display: flex; + align-items: center; + gap: 8px; +} + +.app-sidebar { + grid-row: 2 / 4; + background: var(--bg2); + border-right: 1px solid var(--border); + display: flex; + flex-direction: column; + padding: 16px 0; + overflow-y: auto; +} + +.app-footer { + grid-column: 2 / 3; + grid-row: 3; + display: flex; + align-items: center; + padding: 0 20px; + background: var(--bg2); + border-top: 1px solid var(--border); + font-family: var(--font-mono); + font-size: 10px; + color: var(--text3); + letter-spacing: 0.04em; + gap: 16px; +} + +.sidebar-section-label { + padding: 8px 16px 4px; + font-size: 10px; + font-weight: 500; + letter-spacing: 0.1em; + text-transform: uppercase; + color: var(--text3); +} + +.sidebar-item { + display: flex; + align-items: center; + gap: 8px; + padding: 7px 16px; + font-size: 13px; + color: var(--text2); + cursor: pointer; + border-left: 2px solid transparent; + transition: all 0.12s; + user-select: none; +} +.sidebar-item:hover { background: var(--bg3); color: var(--text); } +.sidebar-item.active { background: var(--bg3); color: var(--accent); border-left-color: var(--accent); } +.sidebar-item .icon { width: 16px; text-align: center; opacity: 0.8; } + +.app-content { + overflow-y: auto; + padding: 24px; + background: var(--bg); +} + +/* ── Metric cards ─────────────────────────────────────────────────────────── */ +.metric-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(140px, 1fr)); + gap: 12px; + margin-bottom: 24px; +} +.metric-card { + background: var(--bg2); + border: 1px solid var(--border); + border-radius: var(--radius-md); + padding: 14px 16px; +} +.metric-card .label { + font-size: 10px; + font-weight: 500; + letter-spacing: 0.08em; + text-transform: uppercase; + color: var(--text3); + margin-bottom: 6px; +} +.metric-card .value { + font-size: 26px; + font-weight: 500; + font-family: var(--font-mono); + color: var(--text); + line-height: 1; +} +.metric-card .value.accent { color: var(--accent); } +.metric-card .value.green { color: var(--green); } +.metric-card .value.amber { color: var(--amber); } +.metric-card .value.red { color: var(--red); } +.metric-card .sub { + font-size: 11px; + color: var(--text3); + margin-top: 4px; +} + +/* ── Section headings ────────────────────────────────────────────────────── */ +.section-header { + display: flex; + align-items: baseline; + gap: 10px; + margin-bottom: 16px; +} +.section-header h2 { + font-size: 16px; + font-weight: 500; + color: var(--text); +} +.section-header .tag { + font-size: 10px; + font-family: var(--font-mono); + color: var(--text3); + border: 1px solid var(--border); + border-radius: 3px; + padding: 1px 6px; +} + +.view-grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(360px, 1fr)); + gap: 20px; +} + +.view-grid > * { + min-width: 0; + overflow: hidden; +} + +.chart-card { + background: var(--bg2); + border: 1px solid var(--border); + border-radius: var(--radius-lg); + padding: 20px; + min-width: 0; + overflow: hidden; +} +.chart-card h3 { + font-size: 13px; + font-weight: 500; + color: var(--text2); + margin-bottom: 4px; +} +.chart-card .finding { + font-size: 12px; + color: var(--text3); + margin-bottom: 16px; + line-height: 1.5; +} +.chart-card .finding strong { color: var(--amber); font-weight: 500; } + +/* ── Buttons ─────────────────────────────────────────────────────────────── */ +.btn { + display: inline-flex; + align-items: center; + gap: 6px; + padding: 6px 12px; + font-family: var(--font-sans); + font-size: 13px; + font-weight: 500; + border-radius: var(--radius-sm); + border: 1px solid var(--border2); + background: var(--bg3); + color: var(--text2); + cursor: pointer; + transition: all 0.12s; +} +.btn:hover { background: var(--bg4); color: var(--text); border-color: var(--border2); } +.btn.primary { background: var(--accent2); color: #fff; border-color: var(--accent2); } +.btn.primary:hover { background: var(--accent); } +.btn.sm { padding: 4px 8px; font-size: 12px; } + +/* ── File loader ─────────────────────────────────────────────────────────── */ +.loader-section { + border: 1px solid var(--border); + border-radius: var(--radius-lg); + margin-bottom: 14px; + overflow: hidden; + transition: border-color 0.15s; +} +.loader-section.drag-over { border-color: var(--accent); } +.loader-section-header { + padding: 14px 18px 12px; + background: var(--bg2); + border-bottom: 1px solid var(--border); +} +.loader-section-header h3 { font-size: 13px; font-weight: 600; color: var(--text); margin: 0 0 4px; } +.loader-section-header p { font-size: 12px; color: var(--text3); margin: 0; line-height: 1.5; } +.loader-section-body { padding: 14px 16px; } + +.drop-zone-compact { + border: 1px dashed var(--border2); + border-radius: var(--radius-md); + padding: 10px 16px; + text-align: center; + color: var(--text3); + font-size: 13px; + cursor: pointer; + transition: all 0.15s; +} +.drop-zone-compact:hover, .drop-zone-compact.drag-over { + border-color: var(--accent); + background: rgba(74, 158, 255, 0.04); + color: var(--text2); +} + +.file-slot { + display: flex; + align-items: center; + gap: 10px; + padding: 9px 14px; + border: 1px solid var(--border); + border-radius: var(--radius-md); + cursor: pointer; + transition: all 0.12s; + margin-bottom: 7px; +} +.file-slot:last-child { margin-bottom: 0; } +.file-slot:hover { border-color: var(--border2); background: var(--bg2); } +.file-slot.loaded { border-color: rgba(61, 184, 122, 0.3); background: rgba(61, 184, 122, 0.03); } +.file-slot-status { width: 28px; text-align: center; font-size: 16px; flex-shrink: 0; } +.file-slot .filename { font-family: var(--font-mono); font-size: 12px; color: var(--text2); overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } +.file-slot .badge { font-size: 10px; padding: 2px 6px; border-radius: 3px; font-weight: 500; flex-shrink: 0; } +.file-slot .badge.ok { background: rgba(61, 184, 122, 0.15); color: var(--green); } +.file-slot .badge.opt { background: rgba(96, 104, 120, 0.2); color: var(--text3); } + +.file-grid { + display: grid; + grid-template-columns: repeat(auto-fill, minmax(260px, 1fr)); + gap: 10px; +} +.file-row { + display: flex; + align-items: center; + gap: 10px; + padding: 10px 14px; + background: var(--bg2); + border: 1px solid var(--border); + border-radius: var(--radius-md); +} +.file-row.loaded { border-color: rgba(61, 184, 122, 0.3); } +.file-row .filename { font-family: var(--font-mono); font-size: 12px; color: var(--text2); flex: 1; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } +.file-row .badge { font-size: 10px; padding: 2px 6px; border-radius: 3px; font-weight: 500; } +.file-row .badge.ok { background: rgba(61, 184, 122, 0.15); color: var(--green); } +.file-row .badge.opt { background: rgba(96, 104, 120, 0.2); color: var(--text3); } + +/* ── Table ───────────────────────────────────────────────────────────────── */ +.data-table { width: 100%; border-collapse: collapse; font-size: 13px; } +.data-table th { + text-align: left; + padding: 8px 12px; + font-size: 10px; + font-weight: 500; + letter-spacing: 0.08em; + text-transform: uppercase; + color: var(--text3); + border-bottom: 1px solid var(--border); +} +.data-table td { + padding: 9px 12px; + border-bottom: 1px solid var(--row-sep); + color: var(--text); +} +.data-table tr:hover td { background: var(--bg3); } +.data-table .mono { font-family: var(--font-mono); font-size: 12px; } +.data-table .pill { + display: inline-block; + padding: 2px 8px; + border-radius: 10px; + font-size: 11px; + font-weight: 500; +} +.pill-blue { background: rgba(74, 158, 255, 0.15); color: var(--accent); } +.pill-green { background: rgba(61, 184, 122, 0.15); color: var(--green); } +.pill-amber { background: rgba(245, 158, 11, 0.15); color: var(--amber); } +.pill-red { background: rgba(224, 82, 82, 0.15); color: var(--red); } +.pill-gray { background: rgba(96, 104, 120, 0.2); color: var(--text3); } + +/* ── Filter bar ─────────────────────────────────────────────────────────── */ +.filter-bar { + display: flex; + align-items: center; + gap: 8px; + flex-wrap: wrap; + margin-bottom: 16px; +} +.filter-bar select, .filter-bar input { + padding: 5px 10px; + background: var(--bg2); + border: 1px solid var(--border2); + border-radius: var(--radius-sm); + color: var(--text); + font-size: 13px; + font-family: var(--font-sans); + outline: none; +} +.filter-bar select:focus, .filter-bar input:focus { border-color: var(--accent); } +.filter-bar label { font-size: 12px; color: var(--text3); } + +/* ── Progress bar ───────────────────────────────────────────────────────── */ +.pbar-bg { background: var(--bg3); border-radius: 2px; height: 6px; width: 100%; } +.pbar-fill { height: 6px; border-radius: 2px; transition: width 0.3s; } + +/* ── Status badge ───────────────────────────────────────────────────────── */ +.status-dot { width: 7px; height: 7px; border-radius: 50%; display: inline-block; margin-right: 5px; } +.dot-green { background: var(--green); } +.dot-amber { background: var(--amber); } +.dot-red { background: var(--red); } +.dot-gray { background: var(--text3); } + +/* ── Scrollbar ───────────────────────────────────────────────────────────── */ +::-webkit-scrollbar { width: 6px; height: 6px; } +::-webkit-scrollbar-track { background: transparent; } +::-webkit-scrollbar-thumb { background: var(--bg4); border-radius: 3px; } + +/* ── Utility ─────────────────────────────────────────────────────────────── */ +.mt-16 { margin-top: 16px; } +.mt-24 { margin-top: 24px; } +.mb-16 { margin-bottom: 16px; } +.text-muted { color: var(--text3); } +.text-mono { font-family: var(--font-mono); } +.full-width-card { grid-column: 1 / -1; } diff --git a/src/chat/actionItems.ts b/src/chat/actionItems.ts new file mode 100644 index 0000000..e1942ce --- /dev/null +++ b/src/chat/actionItems.ts @@ -0,0 +1,117 @@ +import type { LLMConfig } from "./providers"; +import { resolveModel } from "./providers"; +import { fetchWithRetry } from "./fetchWithRetry"; +import LLM_CONFIG from "../config/llm.json"; + +const SYSTEM = + "You are an analyst for Maine DOC corrections education programs. " + + "Given a section label and key metrics, write exactly 1-2 concise, specific, data-driven sentences " + + "as an action recommendation for program managers. Be concrete and actionable. " + + "Output ONLY the recommendation text — no labels, no preamble, no markdown."; + +function buildPrompt(sectionLabel: string, metrics: Record): string { + return `Section: ${sectionLabel}\nMetrics:\n${JSON.stringify(metrics, null, 2)}\n\nWrite the action recommendation.`; +} + +async function callGemini(sectionLabel: string, metrics: Record, apiKey: string, model: string): Promise { + const response = await fetchWithRetry( + `${LLM_CONFIG.endpoints.geminiBase}/${model}:generateContent?key=${apiKey}`, + { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + systemInstruction: { parts: [{ text: SYSTEM }] }, + contents: [{ role: "user", parts: [{ text: buildPrompt(sectionLabel, metrics) }] }], + generationConfig: { temperature: LLM_CONFIG.params.actionItems.temperature, maxOutputTokens: LLM_CONFIG.params.actionItems.maxTokens }, + }), + } + ); + if (!response.ok) throw new Error(`Gemini ${response.status}`); + const body = await response.json(); + return (body.candidates?.[0]?.content?.parts?.[0]?.text ?? "").trim(); +} + +async function callOpenAI(sectionLabel: string, metrics: Record, apiKey: string, model: string): Promise { + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.openai, + { + method: "POST", + headers: { "Content-Type": "application/json", "Authorization": `Bearer ${apiKey}` }, + body: JSON.stringify({ + model, + messages: [ + { role: "system", content: SYSTEM }, + { role: "user", content: buildPrompt(sectionLabel, metrics) }, + ], + temperature: LLM_CONFIG.params.actionItems.temperature, + max_tokens: LLM_CONFIG.params.actionItems.maxTokens, + }), + } + ); + if (!response.ok) throw new Error(`OpenAI ${response.status}`); + const body = await response.json(); + return (body.choices?.[0]?.message?.content ?? "").trim(); +} + +async function callClaude(sectionLabel: string, metrics: Record, apiKey: string, model: string): Promise { + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.claude, + { + method: "POST", + headers: { + "Content-Type": "application/json", + "x-api-key": apiKey, + "anthropic-version": LLM_CONFIG.anthropicVersion, + }, + body: JSON.stringify({ + model, + system: SYSTEM, + messages: [{ role: "user", content: buildPrompt(sectionLabel, metrics) }], + max_tokens: LLM_CONFIG.params.actionItems.maxTokens, + temperature: LLM_CONFIG.params.actionItems.temperature, + }), + } + ); + if (!response.ok) throw new Error(`Claude ${response.status}`); + const body = await response.json(); + return (body.content?.[0]?.text ?? "").trim(); +} + +async function callOllama(sectionLabel: string, metrics: Record, model: string): Promise { + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.ollama, + { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + model, + messages: [ + { role: "system", content: SYSTEM }, + { role: "user", content: buildPrompt(sectionLabel, metrics) }, + ], + temperature: LLM_CONFIG.params.actionItems.temperature, + stream: false, + }), + } + ); + if (!response.ok) { + if (response.status === 404) throw new Error(`Ollama model not found. Run: ollama pull ${model}`); + throw new Error(`Ollama ${response.status}`); + } + const body = await response.json(); + return (body.choices?.[0]?.message?.content ?? "").trim(); +} + +export async function generateActionItem( + sectionLabel: string, + metrics: Record, + config: LLMConfig, +): Promise { + const model = resolveModel(config); + switch (config.provider) { + case "gemini": return callGemini(sectionLabel, metrics, config.apiKey, model); + case "openai": return callOpenAI(sectionLabel, metrics, config.apiKey, model); + case "claude": return callClaude(sectionLabel, metrics, config.apiKey, model); + case "ollama": return callOllama(sectionLabel, metrics, model); + } +} diff --git a/src/chat/aggregator.ts b/src/chat/aggregator.ts new file mode 100644 index 0000000..1559e37 --- /dev/null +++ b/src/chat/aggregator.ts @@ -0,0 +1,153 @@ +import type { + ParsedData, DataPlan, DataRequest, FilterSpec, ComputeSpec, + FlatEnrollment, FlatResident, FlatSession, +} from "../types"; +import { buildFlatEnrollments, buildFlatResidents, buildFlatSessions } from "./flatTables"; + +type FlatRow = Record; + +interface FlatTables { + flat_enrollments: FlatRow[]; + flat_residents: FlatRow[]; + flat_sessions: FlatRow[]; +} + +// Known boolean and numeric fields for value coercion. +const BOOL_FIELDS = new Set(["anyEnrollment", "anyCompletion"]); +const NUM_FIELDS = new Set(["monthsToRelease", "durationMinutes", "activePrograms"]); + +function coerce(value: string | undefined, field: string): unknown { + if (value === undefined || value === null) return undefined; + if (BOOL_FIELDS.has(field)) return value === "true"; + if (NUM_FIELDS.has(field)) return parseFloat(value); + return value; +} + +function matchesFilter(row: FlatRow, f: FilterSpec): boolean { + const val = row[f.field]; + const cv = coerce(f.value, f.field); + + switch (f.op) { + case "eq": return val === cv; + case "neq": return val !== cv; + case "in": { + let arr: string[]; + try { arr = JSON.parse(f.value ?? "[]"); } catch { arr = []; } + return arr.includes(String(val)); + } + case "notNull": return val != null; + case "isNull": return val == null; + case "gt": return typeof val === "number" && typeof cv === "number" && val > cv; + case "lt": return typeof val === "number" && typeof cv === "number" && val < cv; + case "gte": return typeof val === "number" && typeof cv === "number" && val >= cv; + case "lte": return typeof val === "number" && typeof cv === "number" && val <= cv; + default: return true; + } +} + +function applyFilters(rows: FlatRow[], filters: FilterSpec[]): FlatRow[] { + return rows.filter(row => filters.every(f => matchesFilter(row, f))); +} + +function computeOne(rows: FlatRow[], spec: ComputeSpec): number | null { + if (rows.length === 0) return null; + + switch (spec.fn) { + case "count": return rows.length; + + case "rate": { + if (!spec.rateField) return null; + const cv = coerce(spec.rateValue, spec.rateField); + const n = rows.filter(r => r[spec.rateField!] === cv).length; + return rows.length > 0 ? Math.round((n / rows.length) * 1000) / 1000 : null; + } + + case "mean": { + if (!spec.field) return null; + const nums = rows.map(r => Number(r[spec.field!])).filter(n => !isNaN(n)); + return nums.length ? nums.reduce((a, b) => a + b, 0) / nums.length : null; + } + + case "sum": { + if (!spec.field) return null; + return rows.map(r => Number(r[spec.field!])).filter(n => !isNaN(n)).reduce((a, b) => a + b, 0); + } + + case "distinctCount": { + if (!spec.field) return null; + return new Set(rows.map(r => String(r[spec.field!]))).size; + } + + case "median": { + if (!spec.field) return null; + const sorted = rows.map(r => Number(r[spec.field!])).filter(n => !isNaN(n)).sort((a, b) => a - b); + if (!sorted.length) return null; + const mid = Math.floor(sorted.length / 2); + return sorted.length % 2 === 0 ? (sorted[mid - 1] + sorted[mid]) / 2 : sorted[mid]; + } + + default: return null; + } +} + +function executeRequest( + req: DataRequest, + tables: FlatTables, +): { id: string; label: string; data: unknown } { + const allRows = tables[req.table] ?? []; + const filtered = req.filters?.length ? applyFilters(allRows, req.filters) : allRows; + + if (!req.groupBy?.length) { + const result: Record = {}; + for (const spec of req.compute) result[spec.alias] = computeOne(filtered, spec); + return { id: req.id, label: req.label, data: result }; + } + + // Group rows by composite key + const groupMap = new Map(); + for (const row of filtered) { + const key = req.groupBy!.map(f => String(row[f] ?? "null")).join("\x00"); + const bucket = groupMap.get(key); + if (bucket) bucket.push(row); + else groupMap.set(key, [row]); + } + + let groups: Record[] = [...groupMap.entries()].map(([key, rows]) => { + const dims = Object.fromEntries(req.groupBy!.map((f, i) => [f, key.split("\x00")[i]])); + const metrics: Record = {}; + for (const spec of req.compute) metrics[spec.alias] = computeOne(rows, spec); + return { ...dims, ...metrics, _count: rows.length }; + }); + + if (req.sortBy) { + const sortField = req.sortBy; + const dir = req.sortDir === "asc" ? 1 : -1; + groups.sort((a, b) => dir * ((Number(a[sortField]) || 0) - (Number(b[sortField]) || 0))); + } + + if (req.limit) groups = groups.slice(0, req.limit); + return { id: req.id, label: req.label, data: groups }; +} + +export function executeDataPlan( + plan: DataPlan, + data: ParsedData, +): { id: string; label: string; data: unknown }[] { + const tables: FlatTables = { + flat_enrollments: buildFlatEnrollments(data) as unknown as FlatRow[], + flat_residents: buildFlatResidents(data) as unknown as FlatRow[], + flat_sessions: buildFlatSessions(data) as unknown as FlatRow[], + }; + + return plan.data_requests.map(req => { + try { + return executeRequest(req, tables); + } catch { + return { id: req.id, label: req.label, data: { error: "Computation failed — check field names and filter values." } }; + } + }); +} + +// Expose flat table builders for downstream use (e.g. size checks). +export { buildFlatEnrollments, buildFlatResidents, buildFlatSessions }; +export type { FlatEnrollment, FlatResident, FlatSession }; diff --git a/src/chat/contextPacket.ts b/src/chat/contextPacket.ts new file mode 100644 index 0000000..1a01799 --- /dev/null +++ b/src/chat/contextPacket.ts @@ -0,0 +1,81 @@ +import type { ParsedData, MetricSnapshot, ContextPacket, ContextPacketMetric, DataPlan } from "../types"; +import { executeDataPlan } from "./aggregator"; + +const GLOSSARY: Record = { + completion_rate: "Completed enrollments ÷ total enrollments (consistent denominator per request).", + never_engaged: "A resident with no DOC program enrollment, no UL enrollment, and no UL platform session.", + near_release: "A resident with fewer than 24 months to projected release date.", + lsiBand: "Level of Service Inventory risk band: Low | Moderate | High | Maximum.", +}; + +export function buildContextPacket( + plan: DataPlan, + data: ParsedData, + snapshot: MetricSnapshot | null, + question: string, +): ContextPacket { + const results = executeDataPlan(plan, data); + + function isEmptyResult(d: unknown): boolean { + if (d === null || d === undefined) return true; + if (Array.isArray(d)) return d.length === 0; + if (typeof d === "object") { + return Object.values(d as Record).every(v => v === null || v === undefined); + } + return false; + } + + const metrics: ContextPacketMetric[] = []; + const emptyMetricLabels: string[] = []; + for (const r of results) { + if (isEmptyResult(r.data)) { + emptyMetricLabels.push(r.label); + } else { + metrics.push({ metric_id: r.id, label: r.label, data: r.data }); + } + } + + const dataGaps: string[] = []; + if (emptyMetricLabels.length > 0) { + dataGaps.push( + `No data returned for: ${emptyMetricLabels.join("; ")}. ` + + `Filters may be too specific or the source data has no matching records.`, + ); + } + const needsEnrollments = plan.data_requests.some(r => r.table === "flat_enrollments"); + const needsResidents = plan.data_requests.some(r => r.table === "flat_residents"); + const needsSessions = plan.data_requests.some(r => r.table === "flat_sessions"); + + if (needsEnrollments && data.enrollments.length === 0 && data.ulEnrollments.length === 0) { + dataGaps.push("No enrollment data — doc_programs and UnlockEd enrollments are both empty."); + } + if (needsResidents && data.residents.length === 0) { + dataGaps.push("No resident roster — doc_residents is not loaded."); + } + if (needsSessions && data.sessions.length === 0) { + dataGaps.push("No session data — user_session_tracking is not loaded."); + } + + // Warn about UL demographic gaps when the plan groups/filters on demographics + const demographicFields = new Set(["lsiBand", "custodyLevel", "educationLevel", "offenseCategory", "gender"]); + const usesDemographics = plan.data_requests.some(r => + r.groupBy?.some(f => demographicFields.has(f)) || + r.filters?.some(f => demographicFields.has(f.field)) + ); + if (usesDemographics && data.ulEnrollments.length > 0) { + dataGaps.push( + "UL (UnlockEd) enrollments have no resident demographics; LSI/custody/education breakdowns cover DOC program enrollments only.", + ); + } + + return { + snapshot_id: snapshot?.id ?? "unknown", + as_of_date: snapshot?.as_of_date ?? new Date().toISOString().slice(0, 10), + question, + filters: plan.filters, + metrics, + data_gaps: dataGaps, + definitions: GLOSSARY, + router_confidence: plan.confidence, + }; +} diff --git a/src/chat/fetchWithRetry.ts b/src/chat/fetchWithRetry.ts new file mode 100644 index 0000000..b7af509 --- /dev/null +++ b/src/chat/fetchWithRetry.ts @@ -0,0 +1,27 @@ +import LLM_CONFIG from "../config/llm.json"; + +const RETRYABLE_CODES = LLM_CONFIG.retry.retryableCodes; + +export async function fetchWithRetry( + url: string, + init: RequestInit, + maxAttempts = LLM_CONFIG.retry.maxAttempts, +): Promise { + if (maxAttempts < 1) throw new Error("fetchWithRetry: maxAttempts must be ≥ 1"); + let res!: Response; + for (let attempt = 0; attempt < maxAttempts; attempt++) { + if (attempt > 0) { + await new Promise(r => setTimeout(r, LLM_CONFIG.retry.baseDelayMs * 2 ** (attempt - 1))); + } + try { + res = await fetch(url, init); + } catch { + if (url.startsWith("http://localhost") || url.startsWith("http://127.0.0.1")) { + throw new Error("Could not connect to Ollama. Make sure Ollama is running (`ollama serve`)."); + } + throw new Error("Network error: could not reach the AI provider. Check your internet connection."); + } + if (!RETRYABLE_CODES.includes(res.status)) return res; + } + return res; +} diff --git a/src/chat/flatTables.ts b/src/chat/flatTables.ts new file mode 100644 index 0000000..5767ca5 --- /dev/null +++ b/src/chat/flatTables.ts @@ -0,0 +1,89 @@ +import type { ParsedData, FlatEnrollment, FlatResident, FlatSession } from "../types"; + +function toYYYYMM(date: Date | null | undefined): string | null { + if (!date) return null; + if (isNaN(date.getTime())) return null; + return `${date.getFullYear()}-${String(date.getMonth() + 1).padStart(2, "0")}`; +} + +export function buildFlatEnrollments(data: ParsedData): FlatEnrollment[] { + // Resident lookup by DOC ID, used to join demographics onto DOC-source enrollments. + const residentById = new Map(data.residents.map(r => [r.id, r])); + const results: FlatEnrollment[] = []; + + for (const e of data.enrollments) { + const res = residentById.get(e.userId); + results.push({ + enrollmentId: e.enrollmentId, + facilityCode: e.facilityCode, + programName: e.programName, + programType: e.programType, + status: e.status, + source: "doc", + lsiBand: res?.lsiBand ?? null, + custodyLevel: res?.custodyLevel ?? null, + educationLevel: res?.educationLevel ?? null, + offenseCategory: res?.offenseCategory ?? null, + gender: res?.gender ?? null, + enrolledMonth: toYYYYMM(e.enrolledDate), + completionMonth: toYYYYMM(e.completionDate), + }); + } + + // UL enrollments cannot be joined to residents (no shared key in ParsedData). + for (const e of data.ulEnrollments) { + results.push({ + enrollmentId: e.enrollmentId, + facilityCode: e.facilityCode, + programName: e.programName, + programType: e.programType, + status: e.status, + source: "ul", + lsiBand: null, + custodyLevel: null, + educationLevel: null, + offenseCategory: null, + gender: null, + enrolledMonth: toYYYYMM(e.enrolledDate), + completionMonth: toYYYYMM(e.completionDate), + }); + } + + return results; +} + +export function buildFlatResidents(data: ParsedData): FlatResident[] { + const enrolledIds = new Set(data.enrollments.map(e => e.userId)); + const completedIds = new Set( + data.enrollments.filter(e => e.status === "Completed").map(e => e.userId), + ); + const activeCountById = new Map(); + for (const e of data.enrollments) { + if (e.status === "Active") { + activeCountById.set(e.userId, (activeCountById.get(e.userId) ?? 0) + 1); + } + } + + return data.residents.map(r => ({ + id: r.id, + facilityCode: r.facilityCode, + lsiBand: r.lsiBand, + custodyLevel: r.custodyLevel, + educationLevel: r.educationLevel, + offenseCategory: r.offenseCategory, + gender: r.gender, + monthsToRelease: r.monthsToRelease, + anyEnrollment: enrolledIds.has(r.id), + anyCompletion: completedIds.has(r.id), + activePrograms: activeCountById.get(r.id) ?? 0, + })); +} + +export function buildFlatSessions(data: ParsedData): FlatSession[] { + return data.sessions + .map(s => ({ + sessionMonth: s.session_date?.slice(0, 7) ?? "", + durationMinutes: parseFloat(s.duration_minutes) || 0, + })) + .filter(s => s.sessionMonth.length === 7); +} diff --git a/src/chat/prompts.ts b/src/chat/prompts.ts new file mode 100644 index 0000000..70aa706 --- /dev/null +++ b/src/chat/prompts.ts @@ -0,0 +1,360 @@ +import type { ContextPacket } from "../types"; + +// ─── Planner ───────────────────────────────────────────────────────────────── + +export const PLANNER_SYSTEM_INSTRUCTION = `You are a data planner for the MAINE DOC · INSIGHT analytics dashboard. +Your job is to translate a user question into a DataPlan — a set of structured data requests that, when executed against the dashboard's raw data, will produce the information needed to answer the question. + +DATA SCHEMA: + +flat_enrollments — one row per program enrollment + Grouping / filter fields: + facilityCode string e.g. SMWRC, MVCF, MCF, MMF, PCCF, MCCI, CCCF, PCCC + programName string exact program name (e.g. "Adult Basic Education") + programType string Education | Vocational | Behavioral | Reentry | Other + status string Active | Waitlisted | Completed | Dropped + source string doc (DOC programs) | ul (UnlockEd platform) + lsiBand string Low | Moderate | High | Maximum (null for UL-only enrollments) + custodyLevel string resident custody level (null for UL-only) + educationLevel string resident's highest education level (null for UL-only) + offenseCategory string offense category (null for UL-only) + gender string (null for UL-only) + enrolledMonth string YYYY-MM (null if unknown) + completionMonth string YYYY-MM (null if unknown) + +flat_residents — one row per resident + Fields: + facilityCode string + lsiBand string Low | Moderate | High | Maximum + custodyLevel string + educationLevel string + offenseCategory string + gender string + monthsToRelease number months until projected release (null if unknown) + anyEnrollment boolean true if resident has any DOC program enrollment + anyCompletion boolean true if resident has completed at least one program + activePrograms number count of currently active enrollments + +flat_sessions — one row per platform session (UnlockEd; no facility breakdown available) + Fields: + sessionMonth string YYYY-MM + durationMinutes number session length in minutes + +COMPUTE FUNCTIONS: + count no field required — counts rows in group + rate requires rateField + rateValue — proportion where rateField == rateValue + mean requires field — arithmetic mean of a numeric field + sum requires field — total of a numeric field + distinctCount requires field — number of distinct values + median requires field — median of a numeric field + +FILTER OPS: + eq / neq equals / not equals; value: string + in value is JSON-stringified array, e.g. '["Active","Completed"]' + notNull field is not null; no value needed + isNull field is null; no value needed + gt lt gte lte numeric comparison; value: number as string (e.g. "24") + +OUTPUT FORMAT (valid JSON only, no prose, no markdown): +{ + "data_requests": [ + { + "id": string, // short camelCase name, e.g. "completionByFacility" + "label": string, // human-readable description shown to the writer + "table": string, // flat_enrollments | flat_residents | flat_sessions + "filters": [{ "field": string, "op": string, "value": string }], + "groupBy": [string], // omit for statewide / ungrouped aggregates + "compute": [ + { + "alias": string, + "fn": string, + "field": string, // for mean/sum/distinctCount/median + "rateField": string, // for rate + "rateValue": string // for rate + } + ], + "limit": number, // optional + "sortBy": string, // optional result column to sort by + "sortDir": "asc" | "desc" + } + ], + "filters": { "facility": string|null, "program": string|null }, + "confidence": number, + "refusal_reason": string|null, + "nearest_hints": string[] +} + +HARD REFUSALS — return data_requests: [], confidence: 0: + - Questions about individual residents by name or DOC number + - Questions asking for post-release outcomes (employment, housing, recidivism) + - Questions asking to predict or forecast outcomes not in the data + +CONFIDENCE: + 0.9–1.0 data directly and fully answers the question + 0.6–0.89 data partially answers it (e.g. wrong time window, demographic data missing for UL enrollments) + < 0.6 cannot answer; set refusal_reason and populate nearest_hints with 2–3 things the data CAN show + +GRANULARITY RULES: + - Include only the groupBy dimensions the question explicitly asks for + - "Overall" / "statewide" questions → no groupBy + - Cap group-by-programName results at limit: 20 (many programs produce long lists) + - Cross-tabulations (e.g. facilityCode × lsiBand) are fine when the question asks for them + - Note: lsiBand / custodyLevel / educationLevel are only available for doc-source enrollments; + include a filter source: eq "doc" when those fields are part of the analysis + +DATA AVAILABILITY NOTE: + - flat_enrollments joins DOC program participation with resident demographics; UL enrollments have null demographics + - flat_sessions has no facilityCode — statewide session counts only + - "Engagement" means program enrollment and participation (anyEnrollment, activePrograms, status in + flat_residents / flat_enrollments). Use flat_sessions only when a question explicitly asks about + UnlockEd platform usage or time-on-platform. flat_sessions is often not loaded — prefer flat_residents + and flat_enrollments for engagement questions. + - Facility names in questions may not match facility codes exactly (e.g. "Maine State Prison" may refer + to MCF, MMF, or another code). When a facility name is ambiguous, run a statewide query with + facilityCode as a groupBy dimension rather than filtering by an unrecognised code. + +EXAMPLES: + +Q: "What are the completion rates by facility?" +→ { + "data_requests": [{ + "id": "completionByFacility", + "label": "Completion rate by facility", + "table": "flat_enrollments", + "filters": [], + "groupBy": ["facilityCode"], + "compute": [ + {"alias": "total", "fn": "count"}, + {"alias": "completionRate", "fn": "rate", "rateField": "status", "rateValue": "Completed"} + ], + "sortBy": "completionRate", "sortDir": "desc" + }], + "filters": {"facility": null, "program": null}, + "confidence": 0.95, "refusal_reason": null, "nearest_hints": [] + } + +Q: "How many High/Maximum LSI residents at MVCF aren't in any program?" +→ { + "data_requests": [{ + "id": "unenrolledHighLsi", + "label": "High and Maximum LSI residents at MVCF with no enrollment", + "table": "flat_residents", + "filters": [ + {"field": "facilityCode", "op": "eq", "value": "MVCF"}, + {"field": "lsiBand", "op": "in", "value": "[\"High\",\"Maximum\"]"}, + {"field": "anyEnrollment", "op": "eq", "value": "false"} + ], + "groupBy": ["lsiBand"], + "compute": [{"alias": "count", "fn": "count"}] + }], + "filters": {"facility": "MVCF", "program": null}, + "confidence": 0.9, "refusal_reason": null, "nearest_hints": [] + } + +Q: "Completion rate by LSI band for education programs (DOC data only)" +→ { + "data_requests": [{ + "id": "completionByLsi", + "label": "Completion rate by LSI band (education programs, DOC source)", + "table": "flat_enrollments", + "filters": [ + {"field": "programType", "op": "eq", "value": "Education"}, + {"field": "source", "op": "eq", "value": "doc"} + ], + "groupBy": ["lsiBand"], + "compute": [ + {"alias": "total", "fn": "count"}, + {"alias": "completionRate", "fn": "rate", "rateField": "status", "rateValue": "Completed"} + ], + "sortBy": "lsiBand", "sortDir": "asc" + }], + "filters": {"facility": null, "program": null}, + "confidence": 0.95, "refusal_reason": null, "nearest_hints": [] + } + +Q: "How can we improve Maine state prison engagement?" +→ { + "data_requests": [ + { + "id": "engagementByFacility", + "label": "Resident engagement rates by facility", + "table": "flat_residents", + "filters": [], + "groupBy": ["facilityCode"], + "compute": [ + {"alias": "total", "fn": "count"}, + {"alias": "anyEnrollmentRate", "fn": "rate", "rateField": "anyEnrollment", "rateValue": "true"}, + {"alias": "anyCompletionRate", "fn": "rate", "rateField": "anyCompletion", "rateValue": "true"} + ], + "sortBy": "anyEnrollmentRate", "sortDir": "asc" + }, + { + "id": "neverEngagedByFacility", + "label": "Never-engaged count by facility", + "table": "flat_residents", + "filters": [ + {"field": "anyEnrollment", "op": "eq", "value": "false"} + ], + "groupBy": ["facilityCode"], + "compute": [{"alias": "count", "fn": "count"}], + "sortBy": "count", "sortDir": "desc" + }, + { + "id": "enrollmentStatusBreakdown", + "label": "Enrollment status breakdown statewide", + "table": "flat_enrollments", + "filters": [], + "groupBy": ["status"], + "compute": [{"alias": "count", "fn": "count"}] + } + ], + "filters": {"facility": null, "program": null}, + "confidence": 0.85, "refusal_reason": null, "nearest_hints": [] + } + +Q: "Show me John Smith's progress" +→ { + "data_requests": [], + "filters": {}, + "confidence": 0, + "refusal_reason": "Individual resident lookups are not supported — only aggregate metrics are available.", + "nearest_hints": ["Completion rates by facility", "Resident engagement tiers statewide", "Never-enrolled counts by LSI band"] + }`; + +export function buildPlannerUserTurn( + question: string, + hintLabels: string[] = [], + dataEnums?: Record, +): string { + const hintsText = hintLabels.length > 0 + ? `CONTEXT HINTS (user was viewing a section about these topics — prefer data that covers them when relevant):\n${hintLabels.join(", ")}\n\n` + : ""; + + let vocabText = ""; + if (dataEnums && Object.keys(dataEnums).length > 0) { + const lines = Object.entries(dataEnums) + .filter(([, vals]) => vals.length > 0) + .map(([key, vals]) => ` ${key}: ${vals.join(", ")}`); + if (lines.length > 0) { + vocabText = `LIVE VOCABULARY (use these exact values in filters — do not use values not on these lists):\n${lines.join("\n")}\n\n`; + } + } + + return `${vocabText}${hintsText}USER QUESTION: "${question}"`; +} + +// ─── Writer ────────────────────────────────────────────────────────────────── + +export const WRITER_SYSTEM_INSTRUCTION = `You are an analyst writing an insight narrative for Maine DOC education program managers. + +RULES: + +1. SOURCE OF TRUTH: + - Answer only from the metric data provided. Do not introduce facts, baselines, + or comparisons not present in the data. + - Domain vocabulary from the business definitions is fine; outside benchmarks + are not. + +2. CITATIONS: + - Cite every number in square brackets: [metric_id: value]. + - For nested values, use dot notation: [program_completion_rate.ABE: 13.6%]. + - For counts, include the unit: [engagement_tiers.high: 14 users]. + - For trends, cite both endpoints: [monthly_active.jun_2025: 30 users] to + [monthly_active.feb_2026: 5 users]. + +3. PREMISE CHECK: + - If the question assumes a direction the data contradicts (e.g., "why is + engagement up?" when data shows decline), state the correct direction + in the first sentence before answering. + +4. SMALL SAMPLES: + - If a metric value is based on N < 5 users or N < 10 events, describe it + as "limited sample" or "based on N=X" rather than presenting it as a + stable rate. Do not extrapolate from small samples. + +5. CAVEATS: + - If a metric carries a caveat (e.g., unit unconfirmed, batch-timestamp + artifact, process-related zero), surface it in the narrative when it + materially affects interpretation. + +6. UNCERTAINTY: + - If ROUTER CONFIDENCE < 0.7, hedge the framing ("the closest available + metric suggests…") and note what would answer the question more directly. + - If AS OF is more than 60 days before today and the question asks about + "now" / "currently" / "this month," note the staleness. + +7. PRIVACY: + - Never name individual residents. Never use DOC numbers. Aggregates only. + - If any reported count is < 5, say "fewer than 5" rather than the exact + number. + +8. LENGTH AND TONE: + - 2–4 sentences. Plain language for a program manager, not a statistician. + - Lead with the most important finding. + - End with one actionable implication only when the data supports a clear + next step. Skip the implication for single-number lookups or when the + finding is purely descriptive. + +9. EMPTY-METRICS BRANCH (rules 8 and the implication requirement are waived): + - If metrics[] is empty, do not compose a narrative. + - List the data gaps and explain which source files would need to be + loaded to answer the question. + - One short paragraph, no citations needed. + +CITATION FORMAT EXAMPLES: + +Single value: +"Completion rates vary sharply by program type [completion_by_program_type.mental_health: 76.5%] versus [completion_by_program_type.vocational: 0%]." + +Trend: +"Active users peaked at [monthly_active.jun_2025: 30] and fell to [monthly_active.feb_2026: 5] over eight months." + +Small sample: +"Anger Management shows the highest rate [completion.anger_management: 76.5%], though based on a limited sample of 14 enrollees." + +With caveat: +"Financial Literacy attendance is logged but shows [attendance.financial_literacy.present_rate: 0%], which the data team has flagged as a process issue rather than actual non-attendance." + +Premise correction: +"Engagement has actually declined, not grown — active users dropped from [monthly_active.jun_2025: 30] to [monthly_active.feb_2026: 5]. The most recent uptick is concentrated in a single January cohort." + +Empty metrics: +"The available metrics do not cover post-release employment. Answering this would require loading post-release outcome data (employment status, wage records) which is not currently in the platform export."`; + +export function buildWriterUserTurn(packet: ContextPacket): string { + const metricsText = packet.metrics + .map(m => { + const caveatsText = m.caveats && m.caveats.length > 0 + ? `\nCAVEATS: ${m.caveats.join("; ")}` + : ""; + return `### ${m.label} (${m.metric_id})\n${JSON.stringify(m.data, null, 2)}${caveatsText}`; + }) + .join("\n\n"); + + const definitionsText = Object.entries(packet.definitions) + .map(([k, v]) => `- **${k}:** ${v}`) + .join("\n"); + + const gapsText = packet.data_gaps.length > 0 + ? `\n\nDATA GAPS (acknowledge these; do not invent values for them):\n${packet.data_gaps.join("\n")}` + : ""; + + const filterText = Object.keys(packet.filters).length > 0 + ? `\nFOCUS: The user is asking specifically about: ${JSON.stringify(packet.filters)}. Prioritise that slice.\n` + : ""; + + const confidenceText = packet.router_confidence !== undefined + ? `ROUTER CONFIDENCE: ${packet.router_confidence}\n` + : ""; + + return `SNAPSHOT: ${packet.snapshot_id} | AS OF: ${packet.as_of_date} +${confidenceText}${filterText} +QUESTION: "${packet.question}" + +AVAILABLE METRICS: +${metricsText} +${gapsText} + +BUSINESS DEFINITIONS: +${definitionsText}`; +} \ No newline at end of file diff --git a/src/chat/providers.ts b/src/chat/providers.ts new file mode 100644 index 0000000..7099a70 --- /dev/null +++ b/src/chat/providers.ts @@ -0,0 +1,47 @@ +import PROVIDER_MODELS from "../config/models.json"; + +export type LLMProvider = "gemini" | "openai" | "claude" | "ollama"; + +export interface LLMConfig { + provider: LLMProvider; + apiKey: string; + model?: string; +} + +export interface ProviderInfo { + label: string; + models: string[]; + keyPlaceholder: string; + keyHint: string; +} + +export const PROVIDER_INFO: Record = { + gemini: { + label: "Google Gemini", + models: PROVIDER_MODELS.gemini, + keyPlaceholder: "AIza…", + keyHint: "aistudio.google.com", + }, + openai: { + label: "OpenAI", + models: PROVIDER_MODELS.openai, + keyPlaceholder: "sk-…", + keyHint: "platform.openai.com/api-keys", + }, + claude: { + label: "Anthropic Claude", + models: PROVIDER_MODELS.claude, + keyPlaceholder: "sk-ant-…", + keyHint: "console.anthropic.com", + }, + ollama: { + label: "Ollama (local)", + models: PROVIDER_MODELS.ollama, + keyPlaceholder: "", + keyHint: "ollama.ai", + }, +}; + +export function resolveModel(config: LLMConfig): string { + return config.model ?? PROVIDER_INFO[config.provider].models[0]; +} diff --git a/src/chat/refusal.ts b/src/chat/refusal.ts new file mode 100644 index 0000000..7c9bb69 --- /dev/null +++ b/src/chat/refusal.ts @@ -0,0 +1,29 @@ +import type { DataPlan, ChatMessage } from "../types"; + +export function buildRefusalMessage( + plan: DataPlan, + snapshotId: string, +): ChatMessage { + const lines: string[] = []; + + if (plan.refusal_reason) { + lines.push(`I can't answer that from the available data: **${plan.refusal_reason}**`); + } else { + lines.push("I don't have a way to compute what you're asking with the available data."); + } + + if (plan.nearest_hints && plan.nearest_hints.length > 0) { + lines.push("\nYou might try asking about:"); + plan.nearest_hints.forEach(h => lines.push(`- ${h}`)); + } + + return { + id: crypto.randomUUID(), + role: "assistant", + content: lines.join("\n"), + snapshot_id: snapshotId, + is_refusal: true, + nearest_metrics: [], + timestamp: new Date().toISOString(), + }; +} diff --git a/src/chat/router.ts b/src/chat/router.ts new file mode 100644 index 0000000..09664a1 --- /dev/null +++ b/src/chat/router.ts @@ -0,0 +1,280 @@ +import type { DataPlan, ConversationTurn } from "../types"; +import type { LLMConfig } from "./providers"; +import { resolveModel } from "./providers"; +import { fetchWithRetry } from "./fetchWithRetry"; +import { PLANNER_SYSTEM_INSTRUCTION, buildPlannerUserTurn } from "./prompts"; +import LLM_CONFIG from "../config/llm.json"; + +// Gemini structured output schema for DataPlan. +// Gemini doesn't support additionalProperties or union types in responseSchema, +// so filter values are always strings; the aggregator coerces them as needed. +const PLANNER_RESPONSE_SCHEMA = { + type: "object", + properties: { + data_requests: { + type: "array", + items: { + type: "object", + properties: { + id: { type: "string" }, + label: { type: "string" }, + table: { type: "string" }, + filters: { + type: "array", + items: { + type: "object", + properties: { + field: { type: "string" }, + op: { type: "string" }, + value: { type: "string" }, + }, + required: ["field", "op"], + }, + }, + groupBy: { type: "array", items: { type: "string" } }, + compute: { + type: "array", + items: { + type: "object", + properties: { + alias: { type: "string" }, + fn: { type: "string" }, + field: { type: "string" }, + rateField: { type: "string" }, + rateValue: { type: "string" }, + }, + required: ["alias", "fn"], + }, + }, + limit: { type: "number" }, + sortBy: { type: "string" }, + sortDir: { type: "string" }, + }, + required: ["id", "label", "table", "compute"], + }, + }, + filters: { + type: "object", + properties: { + facility: { type: "string" }, + program: { type: "string" }, + }, + }, + confidence: { type: "number" }, + refusal_reason: { type: "string" }, + nearest_hints: { type: "array", items: { type: "string" } }, + }, + required: ["data_requests", "filters", "confidence"], +}; + +function normalizePlan(raw: Partial): DataPlan { + return { + data_requests: raw.data_requests ?? [], + filters: raw.filters ?? {}, + confidence: raw.confidence ?? 0, + refusal_reason: raw.refusal_reason, + nearest_hints: raw.nearest_hints, + }; +} + +async function callPlannerGemini( + question: string, + config: LLMConfig, + hintLabels: string[], + history: ConversationTurn[], + dataEnums?: Record, +): Promise { + const model = resolveModel(config); + const historyContents = history.map(h => ({ + role: h.role === "assistant" ? "model" : "user", + parts: [{ text: h.content }], + })); + const response = await fetchWithRetry( + `${LLM_CONFIG.endpoints.geminiBase}/${model}:generateContent?key=${config.apiKey}`, + { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + systemInstruction: { parts: [{ text: PLANNER_SYSTEM_INSTRUCTION }] }, + contents: [ + ...historyContents, + { role: "user", parts: [{ text: buildPlannerUserTurn(question, hintLabels, dataEnums) }] }, + ], + generationConfig: { + responseMimeType: "application/json", + responseSchema: PLANNER_RESPONSE_SCHEMA, + temperature: LLM_CONFIG.params.planner.temperature, + }, + }), + } + ); + + if (!response.ok) { + if (response.status === 503) throw new Error("The AI model is currently overloaded. Please wait a moment and try again."); + throw new Error(`Planner ${response.status}: ${await response.text()}`); + } + const body = await response.json(); + const text: string = body.candidates?.[0]?.content?.parts?.[0]?.text; + if (!text) throw new Error("Planner returned empty response"); + return normalizePlan(JSON.parse(text)); +} + +async function callPlannerOpenAI( + question: string, + config: LLMConfig, + hintLabels: string[], + history: ConversationTurn[], + dataEnums?: Record, +): Promise { + const sysInstruction = PLANNER_SYSTEM_INSTRUCTION + + "\n\nRespond ONLY with a valid JSON object. No explanation, markdown, or other text."; + + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.openai, + { + method: "POST", + headers: { "Content-Type": "application/json", "Authorization": `Bearer ${config.apiKey}` }, + body: JSON.stringify({ + model: resolveModel(config), + messages: [ + { role: "system", content: sysInstruction }, + ...history.map(h => ({ role: h.role, content: h.content })), + { role: "user", content: buildPlannerUserTurn(question, hintLabels, dataEnums) }, + ], + response_format: { type: "json_object" }, + temperature: LLM_CONFIG.params.planner.temperature, + }), + } + ); + + if (!response.ok) { + if (response.status === 503) throw new Error("The AI model is currently overloaded. Please wait a moment and try again."); + throw new Error(`Planner ${response.status}: ${await response.text()}`); + } + const body = await response.json(); + const text: string = body.choices?.[0]?.message?.content; + if (!text) throw new Error("Planner returned empty response"); + return normalizePlan(JSON.parse(text)); +} + +async function callPlannerClaude( + question: string, + config: LLMConfig, + hintLabels: string[], + history: ConversationTurn[], + dataEnums?: Record, +): Promise { + const sysInstruction = PLANNER_SYSTEM_INSTRUCTION + + "\n\nOutput ONLY a valid JSON object. No explanation, markdown, or other text."; + + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.claude, + { + method: "POST", + headers: { + "Content-Type": "application/json", + "x-api-key": config.apiKey, + "anthropic-version": LLM_CONFIG.anthropicVersion, + }, + body: JSON.stringify({ + model: resolveModel(config), + system: sysInstruction, + messages: [ + ...history.map(h => ({ role: h.role, content: h.content })), + { role: "user", content: buildPlannerUserTurn(question, hintLabels, dataEnums) }, + ], + max_tokens: LLM_CONFIG.params.planner.maxTokens, + temperature: LLM_CONFIG.params.planner.temperature, + }), + } + ); + + if (!response.ok) { + if (response.status === 503) throw new Error("The AI model is currently overloaded. Please wait a moment and try again."); + throw new Error(`Planner ${response.status}: ${await response.text()}`); + } + const body = await response.json(); + const text: string = body.content?.[0]?.text ?? ""; + if (!text) throw new Error("Planner returned empty response"); + + // Extract JSON object even if wrapped in code fences + const jsonMatch = text.match(/```json\s*([\s\S]*?)```/) ?? text.match(/(\{[\s\S]*\})/); + if (!jsonMatch) throw new Error("Planner response contained no valid JSON"); + return normalizePlan(JSON.parse(jsonMatch[1])); +} + +async function callPlannerOllama( + question: string, + config: LLMConfig, + hintLabels: string[], + history: ConversationTurn[], + dataEnums?: Record, +): Promise { + const sysInstruction = PLANNER_SYSTEM_INSTRUCTION + + "\n\nRespond ONLY with a valid JSON object. No explanation, markdown, or other text."; + + for (let attempt = 0; attempt < 2; attempt++) { + const suffix = attempt > 0 + ? "\n\nIMPORTANT: Your previous response was not valid JSON. Respond ONLY with the JSON object, no other text." + : ""; + const userTurn = buildPlannerUserTurn(question, hintLabels, dataEnums) + suffix; + + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.ollama, + { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + model: resolveModel(config), + messages: [ + { role: "system", content: sysInstruction }, + ...history.map(h => ({ role: h.role, content: h.content })), + { role: "user", content: userTurn }, + ], + format: "json", + temperature: LLM_CONFIG.params.planner.temperature, + stream: false, + }), + } + ); + + if (!response.ok) { + if (response.status === 404) { + throw new Error(`Ollama model not found. Run: ollama pull ${resolveModel(config)}`); + } + throw new Error(`Planner ${response.status}: ${await response.text()}`); + } + + const body = await response.json(); + const text: string = body.choices?.[0]?.message?.content ?? ""; + if (!text) throw new Error("Planner returned empty response"); + + try { + const jsonMatch = text.match(/```json\s*([\s\S]*?)```/) ?? text.match(/(\{[\s\S]*\})/); + const jsonText = jsonMatch ? jsonMatch[1] : text; + return normalizePlan(JSON.parse(jsonText)); + } catch { + // fall through to retry + } + } + + throw new Error( + `The local model returned malformed output twice. Try a larger model (e.g. \`ollama pull qwen2.5:14b\`) or rephrase your question.` + ); +} + + +export async function callRouter( + question: string, + config: LLMConfig, + hintLabels: string[] = [], + history: ConversationTurn[] = [], + dataEnums?: Record, +): Promise { + switch (config.provider) { + case "gemini": return callPlannerGemini(question, config, hintLabels, history, dataEnums); + case "openai": return callPlannerOpenAI(question, config, hintLabels, history, dataEnums); + case "claude": return callPlannerClaude(question, config, hintLabels, history, dataEnums); + case "ollama": return callPlannerOllama(question, config, hintLabels, history, dataEnums); + } +} diff --git a/src/chat/writer.ts b/src/chat/writer.ts new file mode 100644 index 0000000..6294399 --- /dev/null +++ b/src/chat/writer.ts @@ -0,0 +1,209 @@ +import type { ContextPacket, ConversationTurn } from "../types"; +import type { LLMConfig } from "./providers"; +import { resolveModel } from "./providers"; +import { fetchWithRetry } from "./fetchWithRetry"; +import { WRITER_SYSTEM_INSTRUCTION, buildWriterUserTurn } from "./prompts"; +import LLM_CONFIG from "../config/llm.json"; + +function extractNumbers(text: string): string[] { + return (text.match(/\b\d+(?:\.\d+)?/g) ?? []).filter(n => parseFloat(n) > 0); +} + +function validateCitations( + response: string, + packet: ContextPacket, +): { citationWarning?: string } { + const responseNums = extractNumbers(response); + const contextText = JSON.stringify(packet.metrics); + const contextNums = new Set(extractNumbers(contextText)); + + // Expand context numbers to include percentage-scaled equivalents of decimals (e.g. 0.821 → "82.1") + // to avoid false positives when the LLM renders a stored rate as a percentage. + const expandedContextNums = new Set(contextNums); + for (const n of contextNums) { + const v = parseFloat(n); + if (v > 0 && v < 1) expandedContextNums.add(String(Math.round(v * 1000) / 10)); + } + + // Heuristic: allow small whole numbers ≤ 10 without validation (counts, dates, etc.) + const suspect = responseNums.filter(n => parseFloat(n) > 10 && !expandedContextNums.has(n)); + + if (suspect.length === 0) return {}; + return { + citationWarning: + `Note: some numbers in this response (${suspect.slice(0, 3).join(", ")}…) were not found in the metric data. Treat with caution.`, + }; +} + +async function callWriterGemini( + packet: ContextPacket, + apiKey: string, + model: string, + history: ConversationTurn[], +): Promise<{ content: string; citationWarning?: string }> { + const historyContents = history.map(h => ({ + role: h.role === "assistant" ? "model" : "user", + parts: [{ text: h.content }], + })); + const response = await fetchWithRetry( + `${LLM_CONFIG.endpoints.geminiBase}/${model}:generateContent?key=${apiKey}`, + { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + systemInstruction: { parts: [{ text: WRITER_SYSTEM_INSTRUCTION }] }, + contents: [ + ...historyContents, + { role: "user", parts: [{ text: buildWriterUserTurn(packet) }] }, + ], + generationConfig: { temperature: LLM_CONFIG.params.writer.temperature, maxOutputTokens: LLM_CONFIG.params.writer.maxOutputTokens }, + }), + } + ); + + if (!response.ok) { + if (response.status === 503) throw new Error("The AI model is currently overloaded. Please wait a moment and try again."); + throw new Error(`Writer ${response.status}: ${await response.text()}`); + } + + const body = await response.json(); + const content: string = body.candidates?.[0]?.content?.parts?.[0]?.text ?? ""; + if (!content) throw new Error("Writer returned empty response"); + + const { citationWarning } = validateCitations(content, packet); + return { content, citationWarning }; +} + +async function callWriterOpenAI( + packet: ContextPacket, + apiKey: string, + model: string, + history: ConversationTurn[], +): Promise<{ content: string; citationWarning?: string }> { + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.openai, + { + method: "POST", + headers: { + "Content-Type": "application/json", + "Authorization": `Bearer ${apiKey}`, + }, + body: JSON.stringify({ + model, + messages: [ + { role: "system", content: WRITER_SYSTEM_INSTRUCTION }, + ...history.map(h => ({ role: h.role, content: h.content })), + { role: "user", content: buildWriterUserTurn(packet) }, + ], + temperature: LLM_CONFIG.params.writer.temperature, + max_tokens: LLM_CONFIG.params.writer.maxTokens, + }), + } + ); + + if (!response.ok) { + if (response.status === 503) throw new Error("The AI model is currently overloaded. Please wait a moment and try again."); + throw new Error(`Writer ${response.status}: ${await response.text()}`); + } + + const body = await response.json(); + const content: string = body.choices?.[0]?.message?.content ?? ""; + if (!content) throw new Error("Writer returned empty response"); + + const { citationWarning } = validateCitations(content, packet); + return { content, citationWarning }; +} + +async function callWriterClaude( + packet: ContextPacket, + apiKey: string, + model: string, + history: ConversationTurn[], +): Promise<{ content: string; citationWarning?: string }> { + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.claude, + { + method: "POST", + headers: { + "Content-Type": "application/json", + "x-api-key": apiKey, + "anthropic-version": LLM_CONFIG.anthropicVersion, + }, + body: JSON.stringify({ + model, + system: WRITER_SYSTEM_INSTRUCTION, + messages: [ + ...history.map(h => ({ role: h.role, content: h.content })), + { role: "user", content: buildWriterUserTurn(packet) }, + ], + temperature: LLM_CONFIG.params.writer.temperature, + max_tokens: LLM_CONFIG.params.writer.maxTokens, + }), + } + ); + + if (!response.ok) { + if (response.status === 503) throw new Error("The AI model is currently overloaded. Please wait a moment and try again."); + throw new Error(`Writer ${response.status}: ${await response.text()}`); + } + + const body = await response.json(); + const content: string = body.content?.[0]?.text ?? ""; + if (!content) throw new Error("Writer returned empty response"); + + const { citationWarning } = validateCitations(content, packet); + return { content, citationWarning }; +} + +async function callWriterOllama( + packet: ContextPacket, + model: string, + history: ConversationTurn[], +): Promise<{ content: string; citationWarning?: string }> { + const response = await fetchWithRetry( + LLM_CONFIG.endpoints.ollama, + { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ + model, + messages: [ + { role: "system", content: WRITER_SYSTEM_INSTRUCTION }, + ...history.map(h => ({ role: h.role, content: h.content })), + { role: "user", content: buildWriterUserTurn(packet) }, + ], + temperature: LLM_CONFIG.params.writer.temperature, + stream: false, + }), + } + ); + + if (!response.ok) { + if (response.status === 404) { + throw new Error(`Ollama model not found. Run: ollama pull ${model}`); + } + throw new Error(`Writer ${response.status}: ${await response.text()}`); + } + + const body = await response.json(); + const content: string = body.choices?.[0]?.message?.content ?? ""; + if (!content) throw new Error("Writer returned empty response"); + + const { citationWarning } = validateCitations(content, packet); + return { content, citationWarning }; +} + + +export async function callWriter( + packet: ContextPacket, + config: LLMConfig, + history: ConversationTurn[] = [], +): Promise<{ content: string; citationWarning?: string }> { + const model = resolveModel(config); + switch (config.provider) { + case "gemini": return callWriterGemini(packet, config.apiKey, model, history); + case "openai": return callWriterOpenAI(packet, config.apiKey, model, history); + case "claude": return callWriterClaude(packet, config.apiKey, model, history); + case "ollama": return callWriterOllama(packet, model, history); + } +} diff --git a/src/components/ChartFootnote.tsx b/src/components/ChartFootnote.tsx new file mode 100644 index 0000000..c50def4 --- /dev/null +++ b/src/components/ChartFootnote.tsx @@ -0,0 +1,31 @@ +import { COLUMN_LABELS } from "../lib/dataQuality"; +import type { MetricQualityEntry } from "../types"; + +interface Props { + entry: MetricQualityEntry; +} + +export function ChartFootnote({ entry }: Props) { + const { effectiveN, totalN, blockingColumn } = entry; + + // Only render when we have a meaningful denominator and a gap exists + if (effectiveN == null || totalN == null || totalN === 0) return null; + if (effectiveN === totalN) return null; + + const label = blockingColumn ? COLUMN_LABELS[blockingColumn] : "complete data"; + const fieldPhrase = blockingColumn ? `known ${label}` : label; + + return ( +
+ Based on {effectiveN.toLocaleString()} of {totalN.toLocaleString()} residents with {fieldPhrase}. +
+ ); +} diff --git a/src/components/DataQualityBanner.tsx b/src/components/DataQualityBanner.tsx new file mode 100644 index 0000000..a66b7a9 --- /dev/null +++ b/src/components/DataQualityBanner.tsx @@ -0,0 +1,53 @@ +import { COLUMN_LABELS } from "../lib/dataQuality"; +import type { MetricQualityEntry } from "../types"; + +interface Props { + entry: MetricQualityEntry; +} + +function buildMessage(entry: MetricQualityEntry): string { + const { blockingColumn, missingPct, effectiveN, totalN } = entry; + if (!blockingColumn) return "Results may be incomplete due to missing data."; + + const label = COLUMN_LABELS[blockingColumn]; + const pct = Math.round((missingPct ?? 0) * 100); + + // Use exact counts when available; fall back to percentage-derived approximation + const missingCount = (totalN != null && effectiveN != null) + ? totalN - effectiveN + : null; + const countPhrase = missingCount != null + ? `${missingCount} of ${totalN} residents` + : `~${pct}% of residents`; + + if (pct >= 20) { + return `Treat as a directional estimate — ${label} is missing for ${countPhrase} (${pct}%). This is a notable gap that may skew results.`; + } + if (pct >= 10) { + return `Results may be incomplete — ${label} is missing for ${countPhrase} (${pct}%). Use with caution.`; + } + return `Results cover most residents — ${label} is missing for ${countPhrase} (${pct}%). The ${100 - pct}% with data are shown.`; +} + +export function DataQualityBanner({ entry }: Props) { + return ( +
+ ⚠ + {buildMessage(entry)} +
+ ); +} diff --git a/src/components/DataUnavailableCard.tsx b/src/components/DataUnavailableCard.tsx new file mode 100644 index 0000000..99e9098 --- /dev/null +++ b/src/components/DataUnavailableCard.tsx @@ -0,0 +1,59 @@ +import { COLUMN_LABELS } from "../lib/dataQuality"; +import type { MetricQualityEntry } from "../types"; + +interface Props { + title: string; + entry: MetricQualityEntry; + suggestion?: string; +} + +function buildBody(entry: MetricQualityEntry): string { + const { blockingColumn, missingPct, thresholdPct } = entry; + if (!blockingColumn) return "Insufficient data to display this chart."; + + const label = COLUMN_LABELS[blockingColumn]; + + // Boolean columns (sessions / attendance) have no threshold + if (thresholdPct === undefined) { + return `This chart requires ${label} data, which has not been loaded.`; + } + + const requiredPct = Math.round((1 - thresholdPct) * 100); + const presentPct = Math.round((1 - (missingPct ?? 0)) * 100); + const presentCount = Math.round(presentPct / 10); // "X out of 10" + + return ( + `This chart requires ${label} data for at least ${requiredPct}% of residents. ` + + `Currently ${presentCount} out of 10 residents have this information (${presentPct}%).` + ); +} + +export function DataUnavailableCard({ title, entry, suggestion }: Props) { + return ( +
+
+ ⊘ + {title} +
+

+ {buildBody(entry)} +

+ {suggestion && ( +

+ {suggestion} +

+ )} +
+ ); +} diff --git a/src/components/FileLoader.tsx b/src/components/FileLoader.tsx new file mode 100644 index 0000000..9bf414b --- /dev/null +++ b/src/components/FileLoader.tsx @@ -0,0 +1,632 @@ +import React, { useState, useCallback, useRef } from "react"; +import Papa from "papaparse"; +import type { CsvFileKey } from "../types"; +import { CSV_FILE_DESCRIPTORS } from "../types"; +import type { DemoScenario } from "../data/mockData"; +import { generateDemoCSVFiles, downloadCSV } from "../lib/demoExport"; + +interface Props { + onLoad: (files: Record) => void; + onDemo: (scenario: DemoScenario) => void; +} + +const CORE_DOC_KEYS: CsvFileKey[] = ["doc_residents", "doc_programs"]; + +const ADVANCED_DOC_KEYS: CsvFileKey[] = [ + "incidents", + "work_assignments", + "case_plan", + "credentials", + "housing_history", +]; + +const DOC_KEYS: CsvFileKey[] = [...CORE_DOC_KEYS, ...ADVANCED_DOC_KEYS]; + +const UL_KEYS: CsvFileKey[] = [ + "users", + "facilities", + "programs", + "program_classes", + "program_class_enrollments", + "program_completions", + "program_class_events", + "program_class_event_attendance", + "user_session_tracking", + "program_credit_types", +]; + +const REF_KEYS: CsvFileKey[] = ["program_crosswalk", "field_mapping"]; + +// Expected columns per dataset (derived from Raw type interfaces) +const EXPECTED_COLUMNS: Partial> = { + doc_residents: ["ID", "STATUS_DESC", "LOCATION_TO", "LOC_TYPE_TO", "ODARA_SCORE", "STATIC99R_SCORE", "LSI_RATING", "HIGHEST_ED_LVL", "CUSTODY_LVL", "Earliest Release Date", "SENTENCE_OFFENSE", "Offense Date", "COUNTY", "LAST_PROGRAM_COMPLETED", "JOB_ASSIGN"], + doc_programs: ["ID", "GENDER", "Facility", "CUSTODY LEVEL", "Housing Unit", "Housing Pod", "Room", "Bed", "Program", "Program Status", "Program Termination Or Completion Date", "Earliest Release Date"], + incidents: ["ID", "Incident Date", "Incident Type", "Severity", "Sanction", "Facility"], + work_assignments: ["ID", "Assignment Type", "Role Title", "Start Date", "End Date", "Transferable Skills Flag", "Facility"], + case_plan: ["ID", "State ID Obtained", "State ID Date", "Job Lined Up", "Housing Plan", "Trust Account Balance", "Savings Goal Met"], + credentials: ["ID", "Credential Type", "Credential Name", "Date Earned", "Issuing Body", "Verified"], + housing_history: ["ID", "Move Date", "Housing Unit", "Pod", "Room", "Bed", "Custody Level", "Move Reason", "Facility"], +}; + + +const FILE_NAME_TO_KEY: Record = { + "doc_residents.csv": "doc_residents", + "doc_residents.xlsx": "doc_residents", + "doc_programs.csv": "doc_programs", + "doc_programs.xlsx": "doc_programs", + "doc_incidents.csv": "incidents", + "doc_incidents.xlsx": "incidents", + "doc_work_assignments.csv": "work_assignments", + "doc_work_assignments.xlsx": "work_assignments", + "doc_case_plan.csv": "case_plan", + "doc_case_plan.xlsx": "case_plan", + "doc_credentials.csv": "credentials", + "doc_credentials.xlsx": "credentials", + "doc_housing_history.csv": "housing_history", + "doc_housing_history.xlsx": "housing_history", + "users.csv": "users", + "facilities.csv": "facilities", + "programs.csv": "programs", + "program_classes.csv": "program_classes", + "program_class_enrollments.csv": "program_class_enrollments", + "program_completions.csv": "program_completions", + "program_class_events.csv": "program_class_events", + "program_class_event_attendance.csv": "program_class_event_attendance", + "user_session_tracking.csv": "user_session_tracking", + "program_credit_types.csv": "program_credit_types", + "program_crosswalk.csv": "program_crosswalk", + "field_mapping.csv": "field_mapping", +}; + +type SectionId = "doc" | "ul" | "ref"; + +function SectionHeader({ + id, title, subtitle, loaded, open, onToggle, +}: { + id: SectionId; title: string; subtitle: string; + loaded: Record; + open: Record; + onToggle: (id: SectionId) => void; +}) { + const keys = id === "doc" ? DOC_KEYS : id === "ul" ? UL_KEYS : REF_KEYS; + const loadedInSection = keys.filter(k => !!loaded[k]).length; + return ( +
onToggle(id)} + role="button" + tabIndex={0} + onKeyDown={e => (e.key === "Enter" || e.key === " ") && onToggle(id)} + > +
+ +

{title}

+ {loadedInSection > 0 && ( + + {loadedInSection} loaded + + )} +
+

{subtitle}

+
+ ); +} + +interface SlotListProps { + keys: CsvFileKey[]; + accept: string; + loaded: Record; + columnWarnings: Partial>; + previews: Partial>; + columnInfoOpen: CsvFileKey | null; + previewOpen: CsvFileKey | null; + slotRefs: React.MutableRefObject>>; + onSlotChange: (key: CsvFileKey) => (e: React.ChangeEvent) => void; + onRemove: (key: CsvFileKey) => void; + onSetPreviewOpen: (key: CsvFileKey | null) => void; + onSetColumnInfoOpen: (key: CsvFileKey | null) => void; +} + +function SlotList({ + keys, accept, loaded, columnWarnings, previews, + columnInfoOpen, previewOpen, slotRefs, + onSlotChange, onRemove, onSetPreviewOpen, onSetColumnInfoOpen, +}: SlotListProps) { + return ( + <> + {keys.map(key => { + const desc = CSV_FILE_DESCRIPTORS[key]; + const file = loaded[key]; + const warnings = columnWarnings[key]; + const preview = previews[key]; + const isInfoOpen = columnInfoOpen === key; + const isPreviewOpen = previewOpen === key; + const expectedCols = EXPECTED_COLUMNS[key]; + + return ( +
+ {/* Main slot row */} +
slotRefs.current[key]?.click()} + title={file ? file.name : undefined} + > + { slotRefs.current[key] = el ?? undefined; }} + type="file" + accept={accept} + style={{ display: "none" }} + onChange={onSlotChange(key)} + /> +
+ {file + ? ✓ + : ○ + } +
+
+
+ {desc.displayName ?? desc.label} +
+
+ {desc.description} +
+
+ + {/* Info icon (always shown for datasets with expected columns) */} + {expectedCols && ( + + )} + + {file ? ( + <> + {/* Preview toggle */} + + loaded + + + ) : ( + optional + )} +
+ + {/* Column validation warning */} + {warnings && warnings.length > 0 && ( +
+ ⚠ Missing expected columns: {warnings.join(", ")}. Are you sure this is the right file? +
+ )} + + {/* Expected columns info panel */} + {isInfoOpen && expectedCols && ( +
+
Expected columns
+
+ {expectedCols.map(col => ( + + {col} + + ))} +
+
+ )} + + {/* Preview table */} + {isPreviewOpen && preview && preview.rows.length > 0 && ( +
+ + + + {preview.headers.map(h => ( + + ))} + + + + {preview.rows.map((row, i) => ( + + {preview.headers.map(h => ( + + ))} + + ))} + +
+ {h} +
+ {row[h] ?? ""} +
+
+ Showing first {preview.rows.length} row{preview.rows.length !== 1 ? "s" : ""} +
+
+ )} +
+ ); + })} + + ); +} + +function Chevron({ open }: { open: boolean }) { + return ( + + + + ); +} + +interface PreviewData { + headers: string[]; + rows: Record[]; +} + +export function FileLoader({ onLoad, onDemo }: Props) { + const [loaded, setLoaded] = useState>({}); + const [dragOver, setDragOver] = useState(null); + const [unknownFiles, setUnknownFiles] = useState([]); + const [open, setOpen] = useState>({ doc: false, ul: false, ref: false }); + const [showAdvanced, setShowAdvanced] = useState(false); + const [demoMode, setDemoMode] = useState(false); + const [downloadingScenario, setDownloadingScenario] = useState(null); + const [columnInfoOpen, setColumnInfoOpen] = useState(null); + const [columnWarnings, setColumnWarnings] = useState>>({}); + const [previewOpen, setPreviewOpen] = useState(null); + const [previews, setPreviews] = useState>>({}); + const slotRefs = useRef>>({}); + const ulInputRef = useRef(null); + + async function validateAndPreview(key: CsvFileKey, file: File): Promise<{ warnings: string[]; preview: PreviewData }> { + return new Promise((resolve) => { + Papa.parse(file, { + header: true, + preview: 6, + skipEmptyLines: true, + complete: (results) => { + const headers = results.meta.fields ?? []; + const expected = EXPECTED_COLUMNS[key] ?? []; + const missing = expected.filter(col => !headers.includes(col)); + const rows = (results.data as Record[]).slice(0, 5); + resolve({ warnings: missing, preview: { headers, rows } }); + }, + error: () => resolve({ warnings: [], preview: { headers: [], rows: [] } }), + }); + }); + } + + const processFileList = useCallback((files: FileList | File[]) => { + const updates: Partial> = {}; + const unknown: string[] = []; + for (const f of Array.from(files)) { + const key = FILE_NAME_TO_KEY[f.name]; + if (key) updates[key] = f; + else unknown.push(f.name); + } + setLoaded(prev => ({ ...prev, ...updates })); + if (unknown.length > 0) setUnknownFiles(unknown); + }, []); + + const handleSlotChange = (key: CsvFileKey) => async (e: React.ChangeEvent) => { + const file = e.target.files?.[0]; + if (!file) return; + e.target.value = ""; + setLoaded(prev => ({ ...prev, [key]: file })); + const { warnings, preview } = await validateAndPreview(key, file); + if (warnings.length > 0) { + setColumnWarnings(prev => ({ ...prev, [key]: warnings })); + } else { + setColumnWarnings(prev => { const n = { ...prev }; delete n[key]; return n; }); + } + setPreviews(prev => ({ ...prev, [key]: preview })); + }; + + const dragHandlers = (section: SectionId) => ({ + onDragOver: (e: React.DragEvent) => { e.preventDefault(); setDragOver(section); }, + onDragLeave: (e: React.DragEvent) => { + if (!e.currentTarget.contains(e.relatedTarget as Node)) setDragOver(null); + }, + onDrop: (e: React.DragEvent) => { + e.preventDefault(); + setDragOver(null); + processFileList(e.dataTransfer.files); + }, + }); + + const removeFile = (key: CsvFileKey) => { + setLoaded(prev => { const n = { ...prev }; delete n[key]; return n; }); + setColumnWarnings(prev => { const n = { ...prev }; delete n[key]; return n; }); + setPreviews(prev => { const n = { ...prev }; delete n[key]; return n; }); + if (previewOpen === key) setPreviewOpen(null); + if (columnInfoOpen === key) setColumnInfoOpen(null); + }; + + const toggleSection = (id: SectionId) => { + setOpen(prev => ({ ...prev, [id]: !prev[id] })); + }; + + const handleDownloadScenario = (scenario: DemoScenario) => { + setDownloadingScenario(scenario); + setTimeout(() => { + const files = generateDemoCSVFiles(scenario); + for (const f of files) downloadCSV(f.filename, f.content); + setDownloadingScenario(null); + }, 10); + }; + + const loadedCount = Object.keys(loaded).length; + const canLoad = loadedCount > 0; + const visibleDocKeys = showAdvanced ? DOC_KEYS : CORE_DOC_KEYS; + + const DEMO_OPTIONS: { scenario: DemoScenario; label: string; description: string }[] = [ + { scenario: "clean", label: "Clean data", description: "Fully populated, no quality issues" }, + { scenario: "messy", label: "Messy data", description: "~33% LSI missing, gaps in education & dates" }, + { scenario: "advanced-missing", label: "Core data only", description: "Messy core data, no incidents or enrichment files" }, + ]; + + return ( +
+ {/* Header row */} +
+
+

Load data files

+ {!demoMode && ( +

+ All files are optional — insights are generated for whatever data is loaded. +

+ )} +
+ {/* Right-side controls */} +
+ {/* Demo mode toggle */} +
+ + {demoMode ? ( + /* Demo mode: three scenario cards */ +
+ {DEMO_OPTIONS.map(({ scenario, label, description }) => ( +
+
+
{label}
+
{description}
+
+
+ + +
+
+ ))} +
+ ) : null} + + {canLoad && !demoMode && ( +
+ +
+ )} + + {!demoMode && ( + <> + {unknownFiles.length > 0 && ( +

+ Unrecognised files (not loaded): {unknownFiles.join(", ")} +

+ )} + + {/* Section 1: DOC data */} +
+ + {open.doc && ( +
+ + {!showAdvanced && ( +
+ Incidents, work assignments, case plans, credentials, and housing history are hidden.{" "} + +
+ )} +
+ )} +
+ + {/* Section 2: UnlockEd platform data */} +
+ + {open.ul && ( +
+
ulInputRef.current?.click()} + > + { if (e.target.files) processFileList(e.target.files); e.target.value = ""; }} + /> + Drop UnlockEd CSV files here or click to browse +
+
+ {UL_KEYS.map(key => { + const desc = CSV_FILE_DESCRIPTORS[key]; + const file = loaded[key]; + return ( +
+
+ {file + ? ✓ + : ○ + } +
+
+
{desc.label}
+
{desc.description}
+
+ + {file ? "loaded" : "optional"} + +
+ ); + })} +
+
+ )} +
+ + {/* Section 3: Mapping & reference (hidden unless showAdvanced) */} + {showAdvanced && ( +
+ + {open.ref && ( +
+ +
+ )} +
+ )} + + )} +
+ ); +} diff --git a/src/components/OmsCards.tsx b/src/components/OmsCards.tsx new file mode 100644 index 0000000..44228d6 --- /dev/null +++ b/src/components/OmsCards.tsx @@ -0,0 +1,620 @@ +import { useMemo, type ReactNode } from "react"; +import { Bar, Doughnut } from "react-chartjs-2"; +import { + Chart as ChartJS, + CategoryScale, + LinearScale, + BarElement, + ArcElement, + Tooltip, + Legend, +} from "chart.js"; +import type { ParsedData } from "../types"; +import { + computeIncidentStats, + computeCredentialStats, + computeWorkAssignmentStats, + computeCasePlanStats, + computeHousingStats, +} from "../lib/analytics"; + +ChartJS.register(CategoryScale, LinearScale, BarElement, ArcElement, Tooltip, Legend); + +const TRAILING_12MO_CUTOFF = new Date(Date.now() - 365 * 24 * 60 * 60 * 1000); + +const C = { + accent: "#4a9eff", + green: "#3db87a", + amber: "#f59e0b", + red: "#e05252", + purple: "#8b5cf6", + teal: "#14b8a6", + gray: "#606878", + text: "#9ca3b0", + border: "rgba(255,255,255,0.08)", +}; + +const CHART_COLORS = [C.accent, C.green, C.amber, C.purple, C.teal, C.red, C.gray]; + +const BASE = { + responsive: true, + maintainAspectRatio: false, + plugins: { + legend: { display: false }, + tooltip: { + backgroundColor: "#141720", + borderColor: "rgba(255,255,255,0.12)", + borderWidth: 1, + titleColor: "#e8eaf0", + bodyColor: "#9ca3b0", + }, + }, + scales: { + x: { grid: { color: C.border }, ticks: { color: C.text, font: { size: 11 } } }, + y: { grid: { color: C.border }, ticks: { color: C.text, font: { size: 11 } } }, + }, +}; + +function KpiChip({ label, value, color }: { label: string; value: string; color?: string }) { + return ( +
+ + {label} + + + {value} + +
+ ); +} + +function ChartBox({ height, children }: { height: number; children: ReactNode }) { + return
{children}
; +} + +function MissingFilePlaceholder({ dataName }: { dataName: string }) { + return ( +
+ Upload {dataName} data to see this insight. +
+ ); +} + +const DisabledAskAI = () => ( + +); + +// ── OmsReentrySnapshot ──────────────────────────────────────────────────────── + +export function OmsReentrySnapshot({ data }: { data: ParsedData }) { + const trailing12mo = useMemo(() => { + if (data.incidents.length === 0) return null; + return data.incidents.filter(i => i.incidentDate && i.incidentDate >= TRAILING_12MO_CUTOFF).length; + }, [data.incidents]); + + const credentialCount = data.credentials.length; + + const stateIdRate = useMemo(() => { + if (data.casePlans.length === 0) return null; + const pct = Math.round((data.casePlans.filter(c => c.stateIdObtained).length / data.casePlans.length) * 100); + return `${pct}%`; + }, [data.casePlans]); + + const jobLinedUpRate = useMemo(() => { + if (data.casePlans.length === 0) return null; + const pct = Math.round((data.casePlans.filter(c => c.jobLinedUp).length / data.casePlans.length) * 100); + return `${pct}%`; + }, [data.casePlans]); + + const savingsGoalRate = useMemo(() => { + if (data.casePlans.length === 0) return null; + const pct = Math.round((data.casePlans.filter(c => c.savingsGoalMet).length / data.casePlans.length) * 100); + return `${pct}%`; + }, [data.casePlans]); + + const earnedHousingRate = useMemo(() => { + if (data.housingMoves.length === 0) return null; + // Most recent move per resident + const latestByResident = new Map(); + for (const move of data.housingMoves) { + const existing = latestByResident.get(move.residentId); + if (!existing || (move.moveDate && existing.moveDate && move.moveDate > existing.moveDate)) { + latestByResident.set(move.residentId, move); + } + } + const latestMoves = Array.from(latestByResident.values()); + const earned = latestMoves.filter(m => m.moveReason.toLowerCase() === "earned").length; + const pct = Math.round((earned / latestMoves.length) * 100); + return `${pct}%`; + }, [data]); + + return ( +
+
+ Reentry readiness +
+
+ + + + 0 ? String(credentialCount) : "—"} color={credentialCount > 0 ? C.teal : C.gray} /> + + +
+
+ ); +} + +// ── OmsIncidentSection ──────────────────────────────────────────────────────── + +export function OmsIncidentSection({ data }: { data: ParsedData }) { + const stats = useMemo(() => computeIncidentStats(data), [data]); + const hasData = data.incidents.length > 0; + + return ( +
+
Incident activity
+
+ {hasData + ? `${stats.total} total · ${stats.trailing12mo} in trailing 12 months` + : "No incident data loaded"} +
+ {!hasData ? ( + + ) : ( + <> +
+ + + + + + +
+ +
+
+
+ Severity split +
+ + + +
+
+
+ Per-resident count distribution +
+ + + +
+
+ + {stats.byType.length > 0 && ( + <> +
+ Incident type breakdown +
+ + t.type), + datasets: [{ + label: "Count", + data: stats.byType.map(t => t.count), + backgroundColor: C.accent, + borderRadius: 3, + }], + }} + options={{ ...BASE, indexAxis: "y" as const }} + /> + + + )} + + {stats.byFacility.length > 0 && ( +
+
+ Incidents by facility +
+ + f.facilityCode), + datasets: [ + { + label: "Major", + data: stats.byFacility.map(f => f.major), + backgroundColor: C.red, + borderRadius: 3, + stack: "s", + }, + { + label: "Minor", + data: stats.byFacility.map(f => f.count - f.major), + backgroundColor: C.amber, + borderRadius: 3, + stack: "s", + }, + ], + }} + options={{ + ...BASE, + indexAxis: "y" as const, + plugins: { ...BASE.plugins, legend: { display: true, labels: { color: C.text, font: { size: 11 } } } }, + }} + /> + +
+ )} + +
+ +
+ + )} +
+ ); +} + +// ── OmsCredentialSection ────────────────────────────────────────────────────── + +export function OmsCredentialSection({ data }: { data: ParsedData }) { + const stats = useMemo(() => computeCredentialStats(data), [data]); + const hasData = data.credentials.length > 0; + + return ( +
+
Credentials
+
+ {hasData + ? `${stats.total} credentials across ${stats.residentCount} residents — ${stats.hasHiSetOrGedCount} have GED or HiSET` + : "No credential data loaded"} +
+ {!hasData ? ( + + ) : ( + <> +
+ + + + + +
+ +
+
+
+ By credential type +
+ + t.type), + datasets: [{ + label: "Count", + data: stats.byType.map(t => t.count), + backgroundColor: CHART_COLORS, + borderRadius: 3, + }], + }} + options={{ ...BASE }} + /> + +
+
+
+ Verification status +
+ + + +
+
+ + {stats.byIssuingBody.length > 0 && ( +
+
+ By issuing body +
+
+ {stats.byIssuingBody.map(b => ( + + {b.body} {b.count} + + ))} +
+
+ )} + +
+ +
+ + )} +
+ ); +} + +// ── OmsWorkSection ──────────────────────────────────────────────────────────── + +export function OmsWorkSection({ data }: { data: ParsedData }) { + const stats = useMemo(() => computeWorkAssignmentStats(data), [data]); + const hasData = data.workAssignments.length > 0; + + return ( +
+
Work assignments
+
+ {hasData + ? `${stats.activeTotal} active assignments — ${stats.transferableActiveRate}% of facility roles have transferable skills` + : "No work assignment data loaded"} +
+ {!hasData ? ( + + ) : ( + <> +
+ + + + + +
+ + {stats.byRole.length > 0 && ( + <> +
+ Active assignments by role +
+ + r.role), + datasets: [{ + label: "Active count", + data: stats.byRole.map(r => r.count), + backgroundColor: C.accent, + borderRadius: 3, + }], + }} + options={{ ...BASE, indexAxis: "y" as const }} + /> + + + )} + +
+ +
+ + )} +
+ ); +} + +// ── OmsCasePlanSection ──────────────────────────────────────────────────────── + +export function OmsCasePlanSection({ data }: { data: ParsedData }) { + const stats = useMemo(() => computeCasePlanStats(data), [data]); + const hasData = data.casePlans.length > 0; + + return ( +
+
Case plan readiness
+
+ {hasData + ? `${stats.total} case plans — ${stats.stateIdRate}% have state ID, ${stats.savingsGoalMetRate}% met savings goal` + : "No case plan data loaded"} +
+ {!hasData ? ( + + ) : ( + <> +
+ + + + + +
+ +
+
+
+ Readiness score distribution (0–4 indicators met) +
+ + `Score ${d.score}`), + datasets: [{ + label: "Residents", + data: stats.readinessDistribution.map(d => d.count), + backgroundColor: [C.red, C.amber, C.amber, C.green, C.green], + borderRadius: 3, + }], + }} + options={{ ...BASE }} + /> + +
+
+
+ Trust account balance distribution +
+ + b.label), + datasets: [{ + label: "Residents", + data: stats.balanceBands.map(b => b.count), + backgroundColor: [C.red, C.amber, C.accent, C.green], + borderRadius: 3, + }], + }} + options={{ ...BASE }} + /> + +
+
+ +
+ +
+ + )} +
+ ); +} + +// ── OmsHousingSection ───────────────────────────────────────────────────────── + +export function OmsHousingSection({ data }: { data: ParsedData }) { + const stats = useMemo(() => computeHousingStats(data), [data]); + const hasData = data.housingMoves.length > 0; + + return ( +
+
Housing & custody transitions
+
+ {hasData + ? `${stats.earnedHousingRate}% of residents earned their most recent housing move — ${stats.communityCustodyCount} in community custody` + : "No housing history data loaded"} +
+ {!hasData ? ( + + ) : ( + <> +
+ + + +
+ +
+
+
+ Current custody level distribution +
+ + d.level), + datasets: [{ + data: stats.custodyDistribution.map(d => d.count), + backgroundColor: CHART_COLORS, + borderWidth: 0, + }], + }} + options={{ + responsive: true, + maintainAspectRatio: false, + plugins: { + legend: { display: true, position: "right" as const, labels: { color: C.text, font: { size: 10 }, boxWidth: 10 } }, + tooltip: BASE.plugins.tooltip, + }, + }} + /> + +
+
+
+ Earned housing rate by facility +
+ + f.facilityCode), + datasets: [{ + label: "Earned %", + data: stats.earnedByFacility.map(f => f.rate), + backgroundColor: C.green, + borderRadius: 3, + }], + }} + options={{ + ...BASE, + scales: { + ...BASE.scales, + y: { ...BASE.scales.y, max: 100, ticks: { ...BASE.scales.y.ticks, callback: (v: number | string) => `${v}%` } }, + }, + }} + /> + +
+
+ +
+ +
+ + )} +
+ ); +} diff --git a/src/components/SmartActionItem.tsx b/src/components/SmartActionItem.tsx new file mode 100644 index 0000000..6571848 --- /dev/null +++ b/src/components/SmartActionItem.tsx @@ -0,0 +1,73 @@ +import { useState, useEffect, useRef } from "react"; +import type { LLMConfig } from "../chat/providers"; +import { generateActionItem } from "../chat/actionItems"; + +const SESSION_KEY = "llm_config"; + +function loadConfig(): LLMConfig | null { + try { + const raw = sessionStorage.getItem(SESSION_KEY); + return raw ? (JSON.parse(raw) as LLMConfig) : null; + } catch { + return null; + } +} + +interface Props { + sectionLabel: string; + metrics: Record; + fallback: string; + marginTop?: number; +} + +export function SmartActionItem({ sectionLabel, metrics, fallback, marginTop = 20 }: Props) { + const [aiText, setAiText] = useState(null); + const metricsRef = useRef(metrics); + const labelRef = useRef(sectionLabel); + + useEffect(() => { + const config = loadConfig(); + if (!config) return; + generateActionItem(labelRef.current, metricsRef.current, config) + .then((text) => { if (text) setAiText(text); }) + .catch(() => {}); + }, []); + + const text = aiText ?? fallback; + + return ( +
+ Action item: + {text} + {aiText !== null && ( + + ✦ AI + + )} +
+ ); +} diff --git a/src/components/chat/ChatDrawer.tsx b/src/components/chat/ChatDrawer.tsx new file mode 100644 index 0000000..37424bf --- /dev/null +++ b/src/components/chat/ChatDrawer.tsx @@ -0,0 +1,578 @@ +import { useState, useRef, useEffect } from "react"; +import type { ParsedData, MetricSnapshot, ChatMessage, PendingPrompt } from "../../types"; +import type { LLMConfig, LLMProvider } from "../../chat/providers"; +import { PROVIDER_INFO } from "../../chat/providers"; +import { METRIC_REGISTRY } from "../../data/schemaRegistry"; +import { buildContextPacket } from "../../chat/contextPacket"; +import { callRouter } from "../../chat/router"; +import { callWriter } from "../../chat/writer"; +import { buildRefusalMessage } from "../../chat/refusal"; +import { DEMO_RESPONSES } from "../../data/demoResponses"; +import type { DemoEntry } from "../../data/demoResponses"; +import CHAT_CONFIG from "../../config/chat.json"; + +const CONFIDENCE_THRESHOLD = CHAT_CONFIG.confidenceThreshold; +const SESSION_KEY = CHAT_CONFIG.sessionKey; + +export const CHAT_DRAWER_WIDTH = CHAT_CONFIG.drawerWidth; + +interface Props { + data: ParsedData | null; + snapshot: MetricSnapshot | null; + pendingPrompt: PendingPrompt | null; + onPromptConsumed: () => void; + onOpenChange?: (open: boolean) => void; + chatDemoMode?: boolean; + onChatDemoModeChange?: (v: boolean) => void; +} + +const PROVIDERS: LLMProvider[] = ["gemini", "openai", "claude", "ollama"]; + +const inputStyle = { + background: "var(--bg3)", border: "1px solid var(--border2)", + borderRadius: "var(--radius-sm)", padding: "8px 10px", + color: "var(--text)", fontSize: 12, outline: "none", + fontFamily: "var(--font-mono)", width: "100%", boxSizing: "border-box" as const, +}; + +function ApiKeySetup({ onSave }: { onSave: (config: LLMConfig) => void }) { + const [provider, setProvider] = useState("gemini"); + const [value, setValue] = useState(""); + const [model, setModel] = useState(PROVIDER_INFO["gemini"].models[0]); + const info = PROVIDER_INFO[provider]; + const isLocal = provider === "ollama"; + + const handleProviderChange = (p: LLMProvider) => { + setProvider(p); + setValue(""); + setModel(PROVIDER_INFO[p].models[0]); + }; + + return ( +
+

Select AI provider:

+
+ {PROVIDERS.map(p => ( + + ))} +
+ {isLocal ? ( + <> +

+ Runs entirely on your machine — no API key or internet connection required. +

+
+

Model

+ setModel(e.target.value)} + /> +

+ Run ollama list to see installed models. +

+
+ +

+ Requires Ollama running locally. See README for setup instructions. +

+ + ) : ( + <> +

+ Enter a {info.label} API key to enable AI chat. The key is stored in this browser session only and sent only to {info.label} with aggregated metrics — no resident data. +

+ setValue(e.target.value)} + onKeyDown={e => e.key === "Enter" && value.trim() && onSave({ provider, apiKey: value.trim(), model })} + style={inputStyle} + /> +
+

Model

+ +
+ +

+ Get a key at {info.keyHint}. +

+ + )} +
+ ); +} + +// Splits assistant content into plain text and cited values. +// "[b1_education_journey_loss.dropoff.0.withdrawalRate: 81.3%]" → bold "81.3%" +function renderWithCitations(text: string): React.ReactNode { + // Strip any unclosed bracket at the end (truncated response mid-citation) + const cleaned = text.replace(/\[[^\]]*$/, "").trimEnd(); + const parts = cleaned.split(/\[([^\]:]+):\s*([^\]]+)\]/g); + const nodes: React.ReactNode[] = []; + for (let i = 0; i < parts.length; i++) { + if (i % 3 === 0) { + nodes.push(parts[i]); + } else if (i % 3 === 2) { + nodes.push({parts[i]}); + } + // i % 3 === 1 is the metric path — discard it + } + return nodes; +} + +function MessageBubble({ msg }: { msg: ChatMessage }) { + const isUser = msg.role === "user"; + return ( +
+
+ 
+          {isUser ? msg.content : renderWithCitations(msg.content)}
+
+ + {msg.is_refusal && msg.nearest_metrics && msg.nearest_metrics.length > 0 && ( +
+ Related metrics: + {msg.nearest_metrics.map(m => ( + + {m.label} + + ))} +
+ )} +
+ + {!isUser && msg.snapshot_id && ( + + snapshot:{msg.snapshot_id.slice(0, 8)} + + )} +
+ ); +} + +function loadStoredConfig(): LLMConfig | null { + // Try current format + const stored = sessionStorage.getItem(SESSION_KEY); + if (stored) { + try { return JSON.parse(stored) as LLMConfig; } catch { /* ignore */ } + } + // Migrate legacy gemini_api_key entry + const legacyKey = sessionStorage.getItem("gemini_api_key"); + if (legacyKey) { + const config: LLMConfig = { provider: "gemini", apiKey: legacyKey }; + sessionStorage.setItem(SESSION_KEY, JSON.stringify(config)); + sessionStorage.removeItem("gemini_api_key"); + return config; + } + return null; +} + +export function ChatDrawer({ data, snapshot, pendingPrompt, onPromptConsumed, onOpenChange, chatDemoMode = false, onChatDemoModeChange }: Props) { + const [isOpen, setIsOpen] = useState(false); + + useEffect(() => { onOpenChange?.(isOpen); }, [isOpen, onOpenChange]); + const [llmConfig, setLlmConfig] = useState(loadStoredConfig); + const [messages, setMessages] = useState([]); + const [inputValue, setInputValue] = useState(""); + const [contextLabel, setContextLabel] = useState(null); + const [pendingHints, setPendingHints] = useState([]); + const [isLoading, setIsLoading] = useState(false); + const bottomRef = useRef(null); + // Demo mode state + const [demoEntries, setDemoEntries] = useState(null); + const [activeDemoEntry, setActiveDemoEntry] = useState(null); + + useEffect(() => { + if (!pendingPrompt) return; + // eslint-disable-next-line react-hooks/set-state-in-effect + setIsOpen(true); + if (chatDemoMode) { + const entries = DEMO_RESPONSES[pendingPrompt.context_label] ?? null; + setDemoEntries(entries); + setActiveDemoEntry(null); + setMessages([]); + } else { + setInputValue(pendingPrompt.text); + setContextLabel(pendingPrompt.context_label); + setPendingHints(pendingPrompt.metric_hint_ids ?? []); + } + onPromptConsumed(); + }, [pendingPrompt, onPromptConsumed, chatDemoMode]); + + useEffect(() => { + bottomRef.current?.scrollIntoView({ behavior: "smooth" }); + }, [messages]); + + const saveConfig = (config: LLMConfig) => { + sessionStorage.setItem(SESSION_KEY, JSON.stringify(config)); + setLlmConfig(config); + }; + const clearConfig = () => { sessionStorage.removeItem(SESSION_KEY); setLlmConfig(null); }; + + const sendMessage = async (override?: string) => { + const text = (override ?? inputValue).trim(); + if (!text || !data || !llmConfig || isLoading) return; + + const hints = pendingHints; + setInputValue(""); + setContextLabel(null); + setPendingHints([]); + + const userMsg: ChatMessage = { + id: crypto.randomUUID(), + role: "user", + content: text, + timestamp: new Date().toISOString(), + }; + setMessages(prev => [...prev, userMsg]); + setIsLoading(true); + + try { + // Convert metric hint IDs to human-readable labels for the planner's context + const hintLabels = hints.map(id => METRIC_REGISTRY.find(m => m.id === id)?.label ?? id); + const history = messages.slice(-4).map(m => ({ role: m.role, content: m.content })); + const plan = await callRouter(text, llmConfig, hintLabels, history, data?.dataEnums); + const snapshotId = snapshot?.id ?? "unknown"; + + if (plan.confidence < CONFIDENCE_THRESHOLD || plan.data_requests.length === 0) { + const refusal = buildRefusalMessage(plan, snapshotId); + setMessages(prev => [...prev, refusal]); + return; + } + + // Infer required source files from which flat tables the plan needs + const missingFiles: string[] = []; + if (plan.data_requests.some(r => r.table === "flat_residents") && data.residents.length === 0) + missingFiles.push("doc_residents (resident roster)"); + if (plan.data_requests.some(r => r.table === "flat_enrollments") && + data.enrollments.length === 0 && data.ulEnrollments.length === 0) + missingFiles.push("doc_programs (program enrollments)"); + if (plan.data_requests.some(r => r.table === "flat_sessions") && data.sessions.length === 0) + missingFiles.push("user_session_tracking"); + + if (missingFiles.length > 0) { + const fileMsg: ChatMessage = { + id: crypto.randomUUID(), + role: "assistant", + content: `To answer this I need: **${missingFiles.join(", ")}**. Upload ${missingFiles.length > 1 ? "these files" : "this file"} on the Load Data page.`, + snapshot_id: snapshotId, + timestamp: new Date().toISOString(), + }; + setMessages(prev => [...prev, fileMsg]); + return; + } + + const packet = buildContextPacket(plan, data, snapshot, text); + const { content, citationWarning } = await callWriter(packet, llmConfig, history); + + const assistantMsg: ChatMessage = { + id: crypto.randomUUID(), + role: "assistant", + content: citationWarning ? `${content}\n\n⚠ ${citationWarning}` : content, + metric_ids: plan.data_requests.map(r => r.id), + snapshot_id: snapshotId, + timestamp: new Date().toISOString(), + }; + setMessages(prev => [...prev, assistantMsg]); + } catch (e: unknown) { + const errMsg = e instanceof Error ? e.message : String(e); + setMessages(prev => [...prev, { + id: crypto.randomUUID(), + role: "assistant", + content: errMsg, + timestamp: new Date().toISOString(), + }]); + } finally { + setIsLoading(false); + } + }; + + const sendDemoMessage = (entry: DemoEntry) => { + const userMsg: ChatMessage = { + id: crypto.randomUUID(), + role: "user", + content: entry.question, + timestamp: new Date().toISOString(), + }; + const assistantMsg: ChatMessage = { + id: crypto.randomUUID(), + role: "assistant", + content: entry.response, + snapshot_id: snapshot?.id ?? "demo", + timestamp: new Date().toISOString(), + }; + setMessages(prev => [...prev, userMsg, assistantMsg]); + setActiveDemoEntry(entry); + setDemoEntries(null); + }; + + const sendDemoFollowUp = (question: string, response: string) => { + const userMsg: ChatMessage = { + id: crypto.randomUUID(), + role: "user", + content: question, + timestamp: new Date().toISOString(), + }; + const assistantMsg: ChatMessage = { + id: crypto.randomUUID(), + role: "assistant", + content: response, + snapshot_id: snapshot?.id ?? "demo", + timestamp: new Date().toISOString(), + }; + setMessages(prev => [...prev, userMsg, assistantMsg]); + setActiveDemoEntry(null); + }; + + const DRAWER_WIDTH = CHAT_DRAWER_WIDTH; + const EXAMPLE_QUESTIONS = [ + "What should I expect at SMWRC?", + "Why is facility completion low?", + "Are we serving high-risk residents in education?", + "Where should we focus this quarter?", + ]; + + const providerLabel = llmConfig ? PROVIDER_INFO[llmConfig.provider].label : "AI"; + const activeModel = llmConfig ? (llmConfig.model ?? PROVIDER_INFO[llmConfig.provider].models[0]) : null; + + return ( + <> + + + {isOpen && ( +
+
+
+ ✦ {providerLabel} + {llmConfig && ( + + {llmConfig.provider === "ollama" ? "local" : "key set"} + + )} + {activeModel && ( + + {activeModel} + + )} +
+
+ {llmConfig && } + +
+
+ + {/* Demo mode toggle */} +
+
+ + Demo mode + + {chatDemoMode && ( + + ON + + )} +
+ +
+ + {!chatDemoMode && llmConfig?.provider === "ollama" && ( +
+ Running locally — no data leaves your machine. +
+ )} + {!chatDemoMode && llmConfig?.provider !== "ollama" && ( +
+ Aggregated metrics only — no resident names or DOC numbers are shared with the API. +
+ )} + {chatDemoMode && ( +
+ Demo active — click "Ask AI" on Facilities or Engagement cards to explore sample insights. +
+ )} + +
+ {!llmConfig && !chatDemoMode ? ( + + ) : ( + <> + {/* Demo: starter questions for a card */} + {chatDemoMode && demoEntries && messages.length === 0 && ( +
+

Sample questions for this card:

+ {demoEntries.map(entry => ( + + ))} +
+ )} + + {/* Normal empty state */} + {!chatDemoMode && messages.length === 0 && ( +
+

Try asking:

+ {EXAMPLE_QUESTIONS.map(q => ( + + ))} +
+ )} + + {/* Demo: no card selected yet */} + {chatDemoMode && !demoEntries && messages.length === 0 && ( +
+

Click "Ask AI" on the

+

Facilities needing attention

+

or

+

Engagement overview

+

card to start.

+
+ )} + + {messages.map(msg => )} + + {/* Demo: follow-up questions after a response */} + {chatDemoMode && activeDemoEntry && activeDemoEntry.followUps.length > 0 && ( +
+

Follow-up questions:

+ {activeDemoEntry.followUps.map(fu => ( + + ))} +
+ )} + + {isLoading && ( +
+ ✦Thinking… +
+ )} +
+ + )} +
+ + {llmConfig && !chatDemoMode && ( +
+ {contextLabel && ( +
+ Context: + + {contextLabel} + + +
+ )} +
+

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+1,8 @@ +// Prevents additional console window on Windows in release +#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")] + +fn main() { + tauri::Builder::default() + .run(tauri::generate_context!()) + .expect("error while running Maine DOC Insight application"); +} diff --git a/src-tauri/tauri.conf.json b/src-tauri/tauri.conf.json new file mode 100644 index 0000000..3600b84 --- /dev/null +++ b/src-tauri/tauri.conf.json @@ -0,0 +1,47 @@ +{ + "$schema": "../node_modules/@tauri-apps/cli/schema.json", + "build": { + "beforeDevCommand": "npm run dev", + "beforeBuildCommand": "npm run build", + "devPath": "http://localhost:5173", + "distDir": "../dist" + }, + "package": { + "productName": "Maine DOC Insight", + "version": "0.1.0" + }, + "tauri": { + "allowlist": { + "dialog": { "open": true }, + "fs": { "readFile": true, "scope": ["**"] } + }, + "bundle": { + "active": true, + "category": "Utility", + "copyright": "", + "deb": { "depends": [] }, + "externalBin": [], + "icon": ["icons/32x32.png", "icons/128x128.png", "icons/128x128@2x.png", "icons/icon.icns", "icons/icon.ico"], + "identifier": "gov.maine.doc.insight", + "longDescription": "Maine DOC Education Program Analytics", + "macOS": { "entitlements": null, "exceptionDomain": "", "frameworks": [], "providerShortName": null, "signingIdentity": null }, + "resources": [], + "shortDescription": "DOC Education Analytics", + "targets": "all", + "windows": { "certificateThumbprint": null, "digestAlgorithm": "sha256", "timestampUrl": "" } + }, + "security": { "csp": null }, + "updater": { "active": false }, + "windows": [ + { + "fullscreen": false, + "height": 900, + "resizable": true, + "title": "Maine DOC Insight", + "width": 1400, + "minWidth": 1024, + "minHeight": 700 + } + ] + } +} diff --git a/src/App.tsx b/src/App.tsx new file mode 100644 index 0000000..6679ff6 --- /dev/null +++ b/src/App.tsx @@ -0,0 +1,282 @@ +import { useState, useCallback, useEffect } from "react"; +import "./app.css"; +import { FileLoader } from "./components/FileLoader"; +import { HomeView } from "./views/HomeView"; +import { ProgramsView } from "./views/ProgramsView"; +import { ResidentsView } from "./views/ResidentsView"; +import { FacilitiesView } from "./views/FacilitiesView"; +import { ComparisonView } from "./views/ComparisonView"; +import { DataQualityView } from "./views/DataQualityView"; +import { ChatDrawer, CHAT_DRAWER_WIDTH } from "./components/chat/ChatDrawer"; +import { ingestFiles } from "./lib/ingest"; +import { generateMockData, type DemoScenario } from "./data/mockData"; +import { DataQualityProvider } from "./contexts/DataQualityContext"; +import type { ParsedData, MetricSnapshot, PendingPrompt, MetricKey, MetricQualityMap } from "./types"; + +type View = "load" | "dashboard" | "quality" | "programs" | "residents" | "facilities" | "comparison"; + +interface NavItem { id: View; label: string; icon: string } + +const NAV_ITEMS: NavItem[] = [ + { id: "dashboard", label: "Home", icon: "◈" }, + { id: "quality", label: "Data Quality", icon: "◎" }, + { id: "programs", label: "Programs", icon: "▦" }, + { id: "residents", label: "Residents", icon: "◉" }, + { id: "facilities", label: "Facilities", icon: "▣" }, + { id: "comparison", label: "DOC & UL", icon: "⇄" }, +]; + +// Metrics that belong to each view — used to compute per-tab quality indicators +const VIEW_METRICS: Partial> = { + dashboard: ["nearReleaseEngagement", "equitySummary", "qolSignals"], + programs: ["programCompletion", "completionByRisk", "attendanceCorrelation", "timeToCompletion", "completionTrends"], + residents: ["completionByEducation", "completionByRiskOffense"], + facilities: ["facilityMixAdjusted"], + comparison: ["docUlGap"], +}; + +function viewQualityStatus( + view: View, + metricQuality: MetricQualityMap, +): "ok" | "warning" | "blocked" { + const keys = VIEW_METRICS[view] ?? []; + const statuses = keys.map(k => metricQuality[k]?.status ?? "ok"); + if (statuses.includes("blocked")) return "blocked"; + if (statuses.includes("warning")) return "warning"; + return "ok"; +} + +function makeSnapshot(_data: ParsedData, signatures: Record): MetricSnapshot { + const today = new Date(); + const asOf = `${today.getFullYear()}-${String(today.getMonth() + 1).padStart(2, "0")}-${String(today.getDate()).padStart(2, "0")}`; + return { + id: crypto.randomUUID(), + computed_at: today.toISOString(), + as_of_date: asOf, + source_signatures: signatures, + schema_version: "v1.0", + }; +} + +export default function App() { + const [view, setView] = useState("load"); + const [data, setData] = useState(null); + const [snapshot, setSnapshot] = useState(null); + const [isDemo, setIsDemo] = useState(false); + const [chatDemoMode, setChatDemoMode] = useState(false); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const [pendingPrompt, setPendingPrompt] = useState(null); + const [chatOpen, setChatOpen] = useState(false); + const [theme, setTheme] = useState<"dark" | "light">(() => + (localStorage.getItem("theme") as "dark" | "light") ?? "dark" + ); + + useEffect(() => { + document.documentElement.setAttribute("data-theme", theme); + localStorage.setItem("theme", theme); + }, [theme]); + + const loadDemo = useCallback((scenario: DemoScenario = "messy") => { + setLoading(true); + setError(null); + setTimeout(() => { + try { + const d = generateMockData(scenario); + setData(d); + setSnapshot(makeSnapshot(d, { demo: "mock-data" })); + setIsDemo(true); + setView(d.qualityReport.overallGrade !== "good" ? "quality" : "dashboard"); + } catch (e) { + setError(String(e)); + } finally { + setLoading(false); + } + }, 50); + }, []); + + const loadFiles = useCallback(async (files: Record) => { + setLoading(true); + setError(null); + try { + const d = await ingestFiles(files); + const sigs: Record = {}; + for (const [name, file] of Object.entries(files)) { + sigs[name] = `${file.size}`; + } + setData(d); + setSnapshot(makeSnapshot(d, sigs)); + setIsDemo(false); + setView(d.qualityReport.overallGrade !== "good" ? "quality" : "dashboard"); + } catch (e) { + setError(`Ingestion error: ${e}`); + } finally { + setLoading(false); + } + }, []); + + const reset = () => { setData(null); setSnapshot(null); setView("load"); setIsDemo(false); setError(null); }; + + return ( +