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\"\n});\n```\n\n## Val Town Utility Functions\n\nVal Town provides several utility functions to help with common project tasks.\n\n### Importing Utilities\n\nAlways import utilities with version pins to avoid breaking changes:\n\n```ts\nimport { parseProject, readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n```\n\n### Available Utilities\n\n\n#### **serveFile** - Serve project files with proper content types\n\nFor example, in Hono:\n\n```ts\n// serve all files in frontend/ and shared/\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n```\n\n#### **readFile** - Read files from within the project:\n\n```ts\n// Read a file from the project\nconst fileContent = await readFile(\"/frontend/index.html\", import.meta.url);\n```\n\n#### **listFiles** - List all files in the project\n\n```ts\nconst files = await listFiles(import.meta.url);\n```\n\n#### **parseProject** - Extract information about the current project from import.meta.url\n\nThis is useful for including info for linking back to a val, ie in \"view source\" urls:\n\n```ts\nconst projectVal = parseProject(import.meta.url);\nconsole.log(projectVal.username); // Owner of the project\nconsole.log(projectVal.name); // Project name\nconsole.log(projectVal.version); // Version number\nconsole.log(projectVal.branch); // Branch name\nconsole.log(projectVal.links.self.project); // URL to the project page\n```\n\nHowever, it's *extremely importing* to note that `parseProject` and other Standard Library utilities ONLY RUN ON THE SERVER.\nIf you need access to this data on the client, run it in the server and pass it to the client by splicing it into the HTML page\nor by making an API request for it.\n\n## Val Town Platform Specifics\n\n- **Redirects:** Use `return new Response(null, { status: 302, headers: { Location: \"/place/to/redirect\" }})` instead of `Response.redirect` which is broken\n- **Images:** Avoid external images or base64 images. Use emojis, unicode symbols, or icon fonts/libraries instead\n- **AI Image:** To inline generate an AI image use: `

`\n- **Storage:** DO NOT use the Deno KV module for storage\n- **Browser APIs:** DO NOT use the `alert()`, `prompt()`, or `confirm()` methods\n- **Weather Data:** Use open-meteo for weather data (doesn't require API keys) unless otherwise specified\n- **View Source:** Add a view source link by importing & using `import.meta.url.replace(\"ems.sh\", \"val.town)\"` (or passing this data to the client) and include `target=\"_top\"` attribute\n- **Error Debugging:** Add `` to HTML to capture client-side errors\n- **Error Handling:** Only use try...catch when there's a clear local resolution; Avoid catches that merely log or return 500s. Let errors bubble up with full context\n- **Environment Variables:** Use `Deno.env.get('keyname')` when you need to, but generally prefer APIs that don't require keys\n- **Imports:** Use `https://esm.sh` for npm and Deno dependencies to ensure compatibility on server and browser\n- **Storage Strategy:** Only use backend storage if explicitly required; prefer simple static client-side sites\n- **React Configuration:** When using React libraries, pin versions with `?deps=react@18.2.0,react-dom@18.2.0` and start the file with `/** @jsxImportSource https://esm.sh/react@18.2.0 */`\n- Ensure all React dependencies and sub-dependencies are pinned to the same version\n- **Styling:** Default to using TailwindCSS via `` unless otherwise specified\n\n## Project Structure and Design Patterns\n\n### Recommended Directory Structure\n```\n├── backend/\n│ ├── database/\n│ │ ├── migrations.ts # Schema definitions\n│ │ ├── queries.ts # DB query functions\n│ │ └── README.md\n│ └── routes/ # Route modules\n│ ├── [route].ts\n│ └── static.ts # Static file serving\n│ ├── index.ts # Main entry point\n│ └── README.md\n├── frontend/\n│ ├── components/\n│ │ ├── App.tsx\n│ │ └── [Component].tsx\n│ ├── favicon.svg\n│ ├── index.html # Main HTML template\n│ ├── index.tsx # Frontend JS entry point\n│ ├── README.md\n│ └── style.css\n├── README.md\n└── shared/\n ├── README.md\n └── utils.ts # Shared types and functions\n```\n\n### Backend (Hono) Best Practices\n\n- Hono is the recommended API framework\n- Main entry point should be `backend/index.ts`\n- **Static asset serving:** Use the utility functions to read and serve project files:\n ```ts\n import { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n \n // serve all files in frontend/ and shared/\n app.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\n app.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n \n // For index.html, often you'll want to bootstrap with initial data\n app.get(\"/\", async c => {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject data to avoid extra round-trips\n const initialData = await fetchInitialData();\n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n });\n ```\n- Create RESTful API routes for CRUD operations\n- Always include this snippet at the top-level Hono app to re-throwing errors to see full stack traces:\n ```ts\n // Unwrap Hono errors to see original error details\n app.onError((err, c) => {\n throw err;\n });\n ```\n\n### Database Patterns\n- Run migrations on startup or comment out for performance\n- Change table names when modifying schemas rather than altering\n- Export clear query functions with proper TypeScript typing\n\n## Common Gotchas and Solutions\n\n1. **Environment Limitations:** \n - Val Town runs on Deno in a serverless context, not Node.js\n - Code in `shared/` must work in both frontend and backend environments\n - Cannot use `Deno` keyword in shared code\n - Use `https://esm.sh` for imports that work in both environments\n\n2. **SQLite Peculiarities:**\n - Limited support for ALTER TABLE operations\n - Create new tables with updated schemas and copy data when needed\n - Always run table creation before querying\n\n3. **React Configuration:**\n - All React dependencies must be pinned to 18.2.0\n - Always include `@jsxImportSource https://esm.sh/react@18.2.0` at the top of React files\n - Rendering issues often come from mismatched React versions\n\n4. **File Handling:**\n - Val Town only supports text files, not binary\n - Use the provided utilities to read files across branches and forks\n - For files in the project, use `readFile` helpers\n\n5. **API Design:**\n - `fetch` handler is the entry point for HTTP vals\n - Run the Hono app with `export default app.fetch // This is the entry point for HTTP 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var(--muted-foreground);\n display: flex;\n align-items: center;\n gap: 0.5rem;\n margin-bottom: 1rem;\n}\n\n.prompts-grid {\n display: grid;\n grid-template-columns: repeat(2, 1fr);\n gap: 1rem;\n width: 100%;\n max-width: 800px;\n}\n\n.prompt-card {\n background-color: var(--card);\n padding: 1.25rem;\n border-radius: var(--radius-lg);\n border: 1px solid transparent;\n text-align: left;\n cursor: pointer;\n transition: all 0.2s ease;\n box-shadow: 0 2px 8px oklch(0 0 0 / 0.04);\n font-size: 0.95rem;\n color: var(--foreground);\n font-weight: 500;\n display: flex;\n align-items: center;\n justify-content: space-between;\n}\n\n.prompt-card:hover {\n transform: translateY(-2px);\n box-shadow: 0 8px 16px oklch(0 0 0 / 0.08);\n border-color: oklch(0 0 0 / 0.05);\n}\n\n/* Energy Question prompt card */\n.eq-prompt-card {\n width: 100%;\n max-width: 800px;\n background: linear-gradient(135deg, oklch(0.95 0.06 145), oklch(0.92 0.08 155));\n border: 1px solid oklch(0.88 0.08 145);\n font-weight: 600;\n color: oklch(0.25 0.08 145);\n margin-bottom: 0.5rem;\n}\n\n.eq-prompt-card:hover {\n background: linear-gradient(135deg, oklch(0.93 0.08 145), oklch(0.90 0.10 155));\n border-color: oklch(0.82 0.10 145);\n box-shadow: 0 8px 20px oklch(0.5 0.15 145 / 0.15);\n}\n\n/* Chat Input */\n.chat-form {\n position: absolute;\n bottom: 2rem;\n left: 50%;\n transform: translateX(-50%);\n width: 100%;\n max-width: 48rem;\n padding: 0 1rem;\n z-index: 10;\n}\n\n.chat-input-container {\n background-color: var(--input-background);\n border-radius: var(--radius-full);\n padding: 0.5rem 0.75rem;\n display: flex;\n align-items: center;\n gap: 0.5rem;\n box-shadow: \n 0 4px 6px -1px oklch(0 0 0 / 0.05),\n 0 2px 4px -1px oklch(0 0 0 / 0.03),\n 0 0 0 1px oklch(0 0 0 / 0.05);\n transition: box-shadow 0.2s ease;\n}\n\n.chat-input-container:focus-within {\n box-shadow: \n 0 10px 15px -3px oklch(0 0 0 / 0.08),\n 0 4px 6px -2px oklch(0 0 0 / 0.04),\n 0 0 0 1px oklch(var(--primary) / 0.3);\n}\n\n.input-action-btn {\n width: 36px;\n height: 36px;\n border-radius: 50%;\n border: none;\n background: transparent;\n color: var(--muted-foreground);\n display: flex;\n align-items: center;\n justify-content: center;\n cursor: pointer;\n transition: all 0.2s;\n}\n\n.input-action-btn:hover {\n background-color: var(--muted);\n color: var(--foreground);\n}\n\n.chat-input {\n flex: 1;\n border: none;\n background: transparent;\n padding: 0.75rem;\n font-size: 1rem;\n font-family: var(--font-sans);\n color: var(--foreground);\n outline: none;\n resize: none;\n max-height: 120px;\n}\n\n.chat-input::placeholder {\n color: var(--muted-foreground);\n}\n\n.send-btn {\n width: 36px;\n height: 36px;\n border-radius: 50%;\n border: none;\n background-color: transparent;\n color: var(--muted-foreground);\n display: flex;\n align-items: center;\n justify-content: center;\n cursor: pointer;\n transition: all 0.2s;\n}\n\n.send-btn:not(:disabled) {\n background-color: var(--primary);\n color: white;\n}\n\n.send-btn:disabled {\n cursor: not-allowed;\n opacity: 0.5;\n}\n\n/* Chat Messages */\n.chat-row {\n display: flex;\n flex-direction: column;\n gap: 0.5rem;\n max-width: 100%;\n}\n\n.chat-row-user {\n align-items: flex-end;\n}\n\n.chat-role {\n font-size: 0.75rem;\n font-weight: 600;\n color: var(--muted-foreground);\n margin-left: 0.5rem;\n margin-bottom: 0.25rem;\n}\n\n.chat-bubble {\n padding: 1rem 1.5rem;\n border-radius: 1.5rem;\n background-color: white;\n color: var(--foreground);\n line-height: 1.6;\n box-shadow: 0 1px 2px oklch(0 0 0 / 0.05);\n max-width: 85%;\n}\n\n.chat-row-user .chat-bubble {\n background-color: var(--muted); /* Light grey for user */\n color: var(--foreground);\n border-bottom-right-radius: 0.25rem;\n}\n\n.chat-row:not(.chat-row-user) .chat-bubble {\n border-bottom-left-radius: 0.25rem;\n background-color: white;\n border: 1px solid var(--sidebar-border);\n box-shadow: 0 2px 4px oklch(0 0 0 / 0.02);\n}\n\n/* Loading & Error */\n.chat-loading {\n text-align: center;\n color: var(--muted-foreground);\n font-size: 0.875rem;\n padding: 1rem;\n}\n\n.chat-error {\n background-color: oklch(0.98 0.02 20);\n color: var(--destructive);\n padding: 1rem;\n border-radius: var(--radius-md);\n margin: 1rem 2rem;\n text-align: center;\n border: 1px solid oklch(0.9 0.05 20);\n}\n\n/* Prose */\n.prose {\n font-size: 1rem;\n}\n.prose p {\n margin-bottom: 0.75em;\n}\n.prose p:last-child {\n margin-bottom: 0;\n}\n.prose code {\n font-family: var(--font-mono);\n font-size: 0.9em;\n background: var(--muted);\n padding: 0.2em 0.4em;\n border-radius: 0.4em;\n}\n.prose pre {\n background: #1e1e1e;\n color: #d4d4d4;\n padding: 1rem;\n border-radius: 0.5rem;\n overflow-x: auto;\n margin: 0.5rem 0;\n}\n\n/* Infrastructure Badge */\n.infra-badge {\n display: flex;\n flex-direction: column;\n align-items: center;\n justify-content: center;\n gap: 0.15rem;\n padding: 0.4rem 1rem;\n background-color: oklch(0.97 0.03 145);\n color: oklch(0.35 0.12 145);\n font-size: 0.7rem;\n font-weight: 500;\n letter-spacing: 0.01em;\n border-bottom: 1px solid oklch(0.92 0.04 145);\n flex-shrink: 0;\n}\n\n.infra-badge-row {\n display: flex;\n align-items: center;\n justify-content: center;\n gap: 0.4rem;\n}\n\n.infra-badge-row svg {\n flex-shrink: 0;\n}\n\n.infra-stats {\n font-size: 0.65rem;\n color: oklch(0.45 0.1 145);\n}\n\n/* Per-message Impact Badge */\n.impact-badge {\n margin-left: 0.5rem;\n max-width: 85%;\n}\n\n.impact-badge-main {\n display: inline-flex;\n align-items: center;\n gap: 0.35rem;\n padding: 0.25rem 0.65rem;\n border: none;\n background-color: oklch(0.97 0.02 145);\n color: oklch(0.4 0.1 145);\n font-size: 0.7rem;\n font-weight: 500;\n border-radius: var(--radius-full);\n cursor: pointer;\n transition: background-color 0.15s ease;\n}\n\n.impact-badge-main:hover {\n background-color: oklch(0.94 0.04 145);\n}\n\n.impact-badge-main svg {\n flex-shrink: 0;\n}\n\n/* Comparison dropdown */\n.impact-comparison {\n margin-top: 0.4rem;\n padding: 0.6rem 0.8rem;\n background-color: var(--card);\n border: 1px solid var(--sidebar-border);\n border-radius: var(--radius-md);\n font-size: 0.7rem;\n}\n\n.comparison-row {\n display: flex;\n justify-content: space-between;\n align-items: center;\n padding: 0.2rem 0;\n}\n\n.comparison-label {\n color: var(--muted-foreground);\n}\n\n.comparison-value {\n font-weight: 600;\n}\n\n.comparison-green {\n color: oklch(0.5 0.18 145);\n}\n\n.comparison-grey {\n color: var(--muted-foreground);\n}\n\n.comparison-savings {\n margin-top: 0.3rem;\n padding-top: 0.3rem;\n border-top: 1px solid var(--sidebar-border);\n color: oklch(0.5 0.18 145);\n font-weight: 600;\n text-align: center;\n}\n\n/* Read more link */\n.impact-read-more {\n display: block;\n margin-top: 0.4rem;\n font-size: 0.65rem;\n color: oklch(0.5 0.15 145);\n text-align: center;\n text-decoration: none;\n}\n\n.impact-read-more:hover {\n text-decoration: underline;\n color: oklch(0.4 0.18 145);\n}\n\n/* Session Footer */\n.chat-bottom-bar {\n position: absolute;\n bottom: 5.5rem;\n left: 50%;\n transform: translateX(-50%);\n width: 100%;\n max-width: 48rem;\n padding: 0 1rem;\n z-index: 9;\n display: flex;\n flex-direction: column;\n align-items: center;\n gap: 0.3rem;\n}\n\n.session-footer {\n display: flex;\n align-items: center;\n justify-content: center;\n gap: 0.35rem;\n padding: 0.35rem 0.75rem;\n background-color: oklch(0.97 0.02 145 / 0.9);\n backdrop-filter: blur(6px);\n color: oklch(0.4 0.1 145);\n font-size: 0.7rem;\n font-weight: 500;\n border-radius: var(--radius-full);\n border: 1px solid oklch(0.92 0.04 145);\n}\n\n.session-footer svg {\n flex-shrink: 0;\n}\n\n.session-equivalent {\n color: oklch(0.5 0.12 145);\n font-style: italic;\n}\n\n/* Carbon Receipt */\n.carbon-receipt {\n display: flex;\n flex-direction: column;\n align-items: center;\n}\n\n.receipt-toggle {\n border: none;\n background: none;\n color: var(--muted-foreground);\n font-size: 0.65rem;\n cursor: pointer;\n text-decoration: underline;\n text-underline-offset: 2px;\n padding: 0.15rem 0.5rem;\n transition: color 0.15s ease;\n}\n\n.receipt-toggle:hover {\n color: var(--foreground);\n}\n\n.receipt-card {\n margin-bottom: 0.4rem;\n padding: 1rem 1.25rem;\n background-color: var(--card);\n border: 1px solid var(--sidebar-border);\n border-radius: var(--radius-lg);\n box-shadow: 0 4px 12px oklch(0 0 0 / 0.06);\n width: 20rem;\n max-width: 100%;\n}\n\n.receipt-title {\n font-size: 0.8rem;\n font-weight: 700;\n color: var(--foreground);\n margin-bottom: 0.6rem;\n text-align: center;\n}\n\n.receipt-rows {\n display: flex;\n flex-direction: column;\n gap: 0.3rem;\n}\n\n.receipt-row {\n display: flex;\n justify-content: space-between;\n font-size: 0.72rem;\n padding: 0.15rem 0;\n border-bottom: 1px solid oklch(0.95 0 0);\n}\n\n.receipt-row span:first-child {\n color: var(--muted-foreground);\n}\n\n.receipt-row span:last-child {\n font-weight: 600;\n color: var(--foreground);\n}\n\n.receipt-equivalent {\n margin-top: 0.6rem;\n padding: 0.4rem 0.6rem;\n background-color: oklch(0.97 0.02 145);\n color: oklch(0.4 0.12 145);\n font-size: 0.7rem;\n font-weight: 500;\n text-align: center;\n border-radius: var(--radius-sm);\n}\n\n.receipt-share {\n margin-top: 0.5rem;\n font-size: 0.65rem;\n color: var(--muted-foreground);\n text-align: center;\n font-style: italic;\n line-height: 1.4;\n}\n\n/* Tool Activity Indicator */\n.tool-activity {\n display: flex;\n align-items: center;\n gap: 0.5rem;\n padding: 0.6rem 1rem;\n background-color: oklch(0.97 0.02 220);\n color: oklch(0.4 0.12 220);\n font-size: 0.8rem;\n font-weight: 500;\n border-radius: var(--radius-md);\n border: 1px solid oklch(0.92 0.04 220);\n animation: tool-pulse 1.5s ease-in-out infinite;\n}\n\n.tool-activity svg {\n flex-shrink: 0;\n}\n\n@keyframes tool-pulse {\n 0%, 100% { opacity: 1; }\n 50% { opacity: 0.6; }\n}\n\n/* Approval Dialog */\n.approval-dialog {\n background-color: var(--card);\n border: 1px solid oklch(0.88 0.06 60);\n border-radius: var(--radius-lg);\n padding: 1.25rem;\n box-shadow: 0 4px 12px oklch(0 0 0 / 0.08);\n max-width: 85%;\n}\n\n.approval-header {\n display: flex;\n align-items: center;\n gap: 0.5rem;\n font-size: 0.85rem;\n font-weight: 700;\n color: oklch(0.45 0.12 60);\n margin-bottom: 0.75rem;\n}\n\n.approval-header svg {\n flex-shrink: 0;\n color: oklch(0.55 0.15 60);\n}\n\n.approval-body {\n margin-bottom: 1rem;\n}\n\n.approval-description {\n font-size: 0.8rem;\n color: var(--muted-foreground);\n margin: 0 0 0.6rem;\n}\n\n.approval-tools {\n display: flex;\n flex-direction: column;\n gap: 0.4rem;\n}\n\n.approval-tool-item {\n padding: 0.5rem 0.75rem;\n background-color: var(--muted);\n border-radius: var(--radius-sm);\n}\n\n.approval-tool-name {\n font-size: 0.8rem;\n font-weight: 600;\n color: var(--foreground);\n}\n\n.approval-tool-args {\n margin: 0.3rem 0 0;\n font-size: 0.7rem;\n font-family: var(--font-mono);\n color: var(--muted-foreground);\n white-space: pre-wrap;\n word-break: break-word;\n}\n\n.approval-actions {\n display: flex;\n gap: 0.5rem;\n}\n\n.approval-btn {\n flex: 1;\n padding: 0.5rem 1rem;\n border: none;\n border-radius: var(--radius-sm);\n font-size: 0.8rem;\n font-weight: 600;\n cursor: pointer;\n transition: all 0.15s ease;\n}\n\n.approve-btn {\n background-color: var(--primary);\n color: var(--primary-foreground);\n}\n\n.approve-btn:hover {\n filter: brightness(1.1);\n}\n\n.reject-btn {\n background-color: var(--muted);\n color: var(--muted-foreground);\n}\n\n.reject-btn:hover {\n background-color: oklch(0.92 0 0);\n color: var(--foreground);\n}\n",[],[201,323],"aa56f030-0cb6-11f1-8b14-42dde27851f2","index.tsx","019c7059-0ed3-724f-869d-645b09fef5a9",["D",1771411410564],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { createRoot } from \"https://esm.sh/react-dom@18.2.0/client\";\nimport { App } from \"./components/App.tsx\";\n\nconst root = document.getElementById(\"root\");\nif (!root) {\n throw new Error(\"No root element found\");\n}\n\ncreateRoot(root).render();",[],[201,332],"0dcff062-0e5b-11f1-ad11-42dde27851f2","index.html","019c7059-0ed3-724f-869d-63b68cc06b7d",84,["D",1771591966166],5,"\n\n \n \n \n React Hono Val Town Starter\n \n \n \n \n \n \n \n

\n \n \n \n\n",[],[201,339],"aa56e6da-0cb6-11f1-8b14-42dde27851f2","favicon.svg","019c7059-0ed3-724f-869d-5ce97042364a",["D",1771411410564],"\n",[],[201,348],"b9678b48-1701-11f1-b1f4-42dde27851f2","App.tsx","019c7059-0ed3-724f-869d-558a3819401a","019c7059-0ed3-724f-869d-5b56e15573c6",154,["D",1772543159804],40,"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport {\n FormEvent,\n KeyboardEvent,\n useEffect,\n useMemo,\n useRef,\n useState,\n} from \"https://esm.sh/react@18.2.0\";\nimport { Streamdown } from \"https://esm.sh/streamdown?deps=react@18.2.0\";\nimport { STARTER_PROMPTS } from \"../../data/prompts.ts\";\n\n// ── Icons ─────────────────────────────────────────────────────────────\n\nconst LogoIcon = () => (\n \n);\n\nconst SendIcon = () => (\n \n);\n\nconst SparklesIcon = () => (\n \n);\n\nconst LeafIcon = () => (\n \n);\n\nconst ToolIcon = () => (\n \n);\n\nconst ShieldIcon = () => (\n \n);\n\n// ── Types ──────────────────────────────────────────────────────────────\n\ntype ChatRole = \"user\" | \"assistant\";\n\ninterface ImpactData {\n version?: string;\n inferenceTime?: { total: number; unit: string };\n energy?: { total: number; unit: string };\n emissions?: { total: number; unit: string };\n}\n\ninterface ChatMessage {\n role: ChatRole;\n content: string;\n impact?: ImpactData;\n}\n\ninterface ChatDoneEvent {\n assistantMessage: string;\n messages: Array<{ role: ChatRole; content: string }>;\n sessionId: string;\n impact?: ImpactData;\n}\n\ninterface ChatChunkEvent {\n delta: string;\n assistantMessage: string;\n}\n\ninterface ChatErrorEvent {\n error: string;\n}\n\ninterface ToolCallInfo {\n id: string;\n name: string;\n arguments: Record;\n}\n\ninterface ToolStartEvent {\n toolName: string;\n toolCallId: string;\n}\n\ninterface ToolResultEvent {\n toolName: string;\n toolCallId: string;\n result: string;\n}\n\ninterface ApprovalRequiredEvent {\n toolCalls: ToolCallInfo[];\n messages: Array<{ role: ChatRole; content: string }>;\n sessionId: string;\n}\n\n// ── Energy Question types ──────────────────────────────────────────────\n\ntype EQFlowStep = \"collect_answer\" | \"collect_email\" | \"done\";\n\ninterface EQFlowState {\n step: EQFlowStep;\n week: number;\n questionText: string;\n rawAnswer?: string;\n email?: string;\n emailRetryCount?: number;\n}\n\ninterface ActiveEQResponse {\n active: boolean;\n state?: EQFlowState;\n messages?: Array<{ role: ChatRole; content: string }>;\n}\n\ninterface EQStepResponse {\n state: EQFlowState;\n messages: Array<{ role: ChatRole; content: string }>;\n}\n\ninterface ActiveTool {\n toolCallId: string;\n toolName: string;\n status: \"running\" | \"completed\";\n}\n\ninterface SessionImpact {\n totalEmissionsUg: number;\n totalEnergyWms: number;\n totalTimeMs: number;\n messageCount: number;\n}\n\ninterface GlobalStats {\n totalMessages: number;\n totalEmissionsUg: number;\n totalEnergyWms: number;\n totalTimeMs: number;\n}\n\n// ── Tool display names ────────────────────────────────────────────────\n\nconst TOOL_DISPLAY_NAMES: Record = {\n get_current_time: \"Getting current time\",\n calculate_carbon_comparison: \"Calculating carbon comparison\",\n web_search: \"Searching the web\",\n};\n\n// ── Helpers ────────────────────────────────────────────────────────────\n\nfunction formatEmissions(ug: number): string {\n if (ug < 1000) return `${Math.round(ug)} \\u00b5g CO\\u2082e`;\n const mg = ug / 1000;\n if (mg < 1000) return `${mg.toFixed(1)} mg CO\\u2082e`;\n return `${(mg / 1000).toFixed(2)} g CO\\u2082e`;\n}\n\nfunction formatEnergy(wms: number): string {\n const wh = wms / 3_600_000;\n if (wh < 0.01) return `${(wh * 1000).toFixed(1)} mWh`;\n return `${wh.toFixed(2)} Wh`;\n}\n\nfunction formatTime(ms: number): string {\n if (ms < 1000) return `${Math.round(ms)} ms`;\n return `${(ms / 1000).toFixed(1)}s`;\n}\n\nfunction getRelatableEquivalent(totalUg: number): string {\n if (totalUg < 100) return \"Less CO\\u2082 than a single breath\";\n if (totalUg < 1000) return \"Less than 1 second of a light bulb\";\n if (totalUg < 10_000) return \"Equivalent to walking 1 cm\";\n if (totalUg < 100_000) return \"Less than 1 second of watching TV\";\n return \"Less than a minute of a light bulb\";\n}\n\nfunction getConventionalEstimate(ug: number): number {\n return Math.round(ug / 0.6);\n}\n\nfunction parseSSEBuffer(buffer: string): {\n parsed: Array<{ type: string; data: string }>;\n remaining: string;\n} {\n const events: Array<{ type: string; data: string }> = [];\n const parts = buffer.split(\"\\n\\n\");\n for (let i = 0; i < parts.length - 1; i++) {\n const lines = parts[i].split(\"\\n\");\n let eventType = \"message\";\n let data = \"\";\n for (const line of lines) {\n if (line.startsWith(\"event: \")) eventType = line.slice(7).trim();\n else if (line.startsWith(\"data: \")) data = line.slice(6);\n }\n events.push({ type: eventType, data });\n }\n return { parsed: events, remaining: parts[parts.length - 1] };\n}\n\n// ── Sub-components ────────────────────────────────────────────────────\n\nfunction ImpactBadge({ impact }: { impact: ImpactData }) {\n const [showComparison, setShowComparison] = useState(false);\n const emissionsUg = impact.emissions?.total ?? 0;\n const energyWms = impact.energy?.total ?? 0;\n const timeMs = impact.inferenceTime?.total ?? 0;\n const conventional = getConventionalEstimate(emissionsUg);\n\n return (\n

\n setShowComparison(!showComparison)}\n title=\"Powered by 100% renewable energy. Click for comparison.\"\n >\n \n \n {formatEmissions(emissionsUg)} · {formatEnergy(energyWms)} · {formatTime(timeMs)}\n \n \n {showComparison && (\n

\n GreenPT (renewable)\n {formatEmissions(emissionsUg)}\n

\n Conventional estimate\n ~{formatEmissions(conventional)}\n

\n {Math.round(((conventional - emissionsUg) / conventional) * 100)}% less CO{\"\\u2082\"} with GreenPT\n

\n )}\n

\n );\n}\n\nfunction SessionFooter({ session }: { session: SessionImpact }) {\n if (session.messageCount === 0) return null;\n return (\n

\n \n \n Session: {session.messageCount} {session.messageCount === 1 ? \"message\" : \"messages\"} · {formatEmissions(session.totalEmissionsUg)} · {formatEnergy(session.totalEnergyWms)} total\n \n — {getRelatableEquivalent(session.totalEmissionsUg)}\n

\n );\n}\n\nfunction CarbonReceipt({ session }: { session: SessionImpact }) {\n const [expanded, setExpanded] = useState(false);\n if (session.messageCount === 0) return null;\n\n return (\n

\n {expanded && (\n

Carbon Receipt

Messages{session.messageCount}

Total CO{\"\\u2082\"}{formatEmissions(session.totalEmissionsUg)}

Total energy{formatEnergy(session.totalEnergyWms)}

Total inference time{formatTime(session.totalTimeMs)}

Conventional estimate~{formatEmissions(getConventionalEstimate(session.totalEmissionsUg))}

{getRelatableEquivalent(session.totalEmissionsUg)}

My AI conversation used only {formatEmissions(session.totalEmissionsUg)} — powered by GreenPT

\n )}\n \n

\n );\n}\n\nfunction InfrastructureBadge({ globalStats }: { globalStats: GlobalStats | null }) {\n return (\n

\n \n Hosted on 100% renewable energy · Scaleway, Paris · EU data residency\n

\n {globalStats && globalStats.totalMessages > 0 && (\n

\n \n {globalStats.totalMessages} {globalStats.totalMessages === 1 ? \"message\" : \"messages\"} served · {formatEmissions(globalStats.totalEmissionsUg)} CO{\"\\u2082\"} total\n \n

\n )}\n

\n );\n}\n\nfunction ToolActivity({ tools }: { tools: ActiveTool[] }) {\n const running = tools.filter((t) => t.status === \"running\");\n if (running.length === 0) return null;\n\n return (\n

\n \n {running.map((t) => TOOL_DISPLAY_NAMES[t.toolName] ?? t.toolName).join(\", \")}...\n

\n );\n}\n\nfunction ApprovalDialog({\n toolCalls,\n onApprove,\n onReject,\n}: {\n toolCalls: ToolCallInfo[];\n onApprove: () => void;\n onReject: () => void;\n}) {\n return (\n

\n \n Tool approval required\n

\n The AI wants to use the following tool{toolCalls.length > 1 ? \"s\" : \"\"}:\n

\n {toolCalls.map((tc) => (\n

\n {TOOL_DISPLAY_NAMES[tc.name] ?? tc.name}\n {Object.keys(tc.arguments).length > 0 && (\n

{JSON.stringify(tc.arguments, null, 2)}

\n )}\n

\n ))}\n

\n \n \n

\n );\n}\n\n// ── Main App ──────────────────────────────────────────────────────────\n\nexport function App() {\n const [messages, setMessages] = useState([]);\n const [input, setInput] = useState(\"\");\n const [isLoading, setIsLoading] = useState(false);\n const [sessionId, setSessionId] = useState(null);\n const [errorMessage, setErrorMessage] = useState(null);\n const [activeTools, setActiveTools] = useState([]);\n const [pendingApproval, setPendingApproval] = useState<{\n toolCalls: ToolCallInfo[];\n messages: Array<{ role: ChatRole; content: string }>;\n sessionId: string;\n } | null>(null);\n const [sessionImpact, setSessionImpact] = useState({\n totalEmissionsUg: 0,\n totalEnergyWms: 0,\n totalTimeMs: 0,\n messageCount: 0,\n });\n const [globalStats, setGlobalStats] = useState(null);\n const [eqFlowState, setEqFlowState] = useState(null);\n const [eqLoading, setEqLoading] = useState(false);\n const [activeEQ, setActiveEQ] = useState(null);\n const messagesContainerRef = useRef(null);\n const hasStartedChat = messages.length > 0;\n\n const canSend = useMemo(\n () => input.trim().length > 0 && !isLoading && !pendingApproval && !eqLoading,\n [input, isLoading, pendingApproval, eqLoading],\n );\n\n useEffect(() => {\n if (messagesContainerRef.current) {\n messagesContainerRef.current.scrollTop = messagesContainerRef.current.scrollHeight;\n }\n }, [messages, isLoading, activeTools, pendingApproval]);\n\n useEffect(() => {\n fetch(\"/api/stats\")\n .then((res) => res.json())\n .then((data: GlobalStats) => setGlobalStats(data))\n .catch(() => {});\n\n fetch(\"/api/energy-question\")\n .then((res) => res.json())\n .then((data: ActiveEQResponse) => setActiveEQ(data))\n .catch(() => {});\n }, []);\n\n function startEQFlow() {\n if (!activeEQ?.active || !activeEQ.state || !activeEQ.messages) return;\n setEqFlowState(activeEQ.state);\n const eqMessages: ChatMessage[] = activeEQ.messages.map((m) => ({\n role: m.role,\n content: m.content,\n }));\n setMessages(eqMessages);\n }\n\n async function handleEQResponse(userInput: string) {\n if (!eqFlowState) return;\n const userMsg: ChatMessage = { role: \"user\", content: userInput };\n const updatedMessages = [...messages, userMsg];\n setMessages(updatedMessages);\n setInput(\"\");\n setEqLoading(true);\n setErrorMessage(null);\n\n try {\n const response = await fetch(\"/api/energy-question/respond\", {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({ flowState: eqFlowState, userInput }),\n });\n\n if (!response.ok) {\n const errorBody = (await response.json()) as { error?: string };\n throw new Error(errorBody.error ?? \"Failed to process response\");\n }\n\n const data = (await response.json()) as EQStepResponse;\n const assistantMessages: ChatMessage[] = data.messages.map((m) => ({\n role: m.role,\n content: m.content,\n }));\n setMessages([...updatedMessages, ...assistantMessages]);\n\n if (data.state.step === \"done\") {\n setEqFlowState(null);\n } else {\n setEqFlowState(data.state);\n }\n } catch (error) {\n const message = error instanceof Error ? error.message : \"Failed to process response\";\n setErrorMessage(message);\n } finally {\n setEqLoading(false);\n }\n }\n\n async function handleSSEStream(response: Response, baseMessages: ChatMessage[]) {\n setMessages([...baseMessages, { role: \"assistant\", content: \"\" }]);\n\n const reader = response.body!.getReader();\n const decoder = new TextDecoder();\n let buffer = \"\";\n\n while (true) {\n const { done, value } = await reader.read();\n if (done) break;\n\n buffer += decoder.decode(value, { stream: true });\n const { parsed, remaining } = parseSSEBuffer(buffer);\n buffer = remaining;\n\n for (const event of parsed) {\n if (event.type === \"thinking\") {\n // LLM is starting a new call — reset displayed content\n setMessages([...baseMessages, { role: \"assistant\", content: \"\" }]);\n } else if (event.type === \"chunk\") {\n const data = JSON.parse(event.data) as ChatChunkEvent;\n setMessages([...baseMessages, { role: \"assistant\", content: data.assistantMessage }]);\n } else if (event.type === \"post_process_start\") {\n // Post-processing — reset content for the refined version\n setMessages([...baseMessages, { role: \"assistant\", content: \"\" }]);\n } else if (event.type === \"tool_start\") {\n const data = JSON.parse(event.data) as ToolStartEvent;\n setActiveTools((prev) => [...prev, { toolCallId: data.toolCallId, toolName: data.toolName, status: \"running\" }]);\n } else if (event.type === \"tool_result\") {\n const data = JSON.parse(event.data) as ToolResultEvent;\n setActiveTools((prev) =>\n prev.map((t) => (t.toolCallId === data.toolCallId ? { ...t, status: \"completed\" as const } : t)),\n );\n } else if (event.type === \"approval_required\") {\n const data = JSON.parse(event.data) as ApprovalRequiredEvent;\n setPendingApproval(data);\n // Remove the empty assistant placeholder\n setMessages(baseMessages);\n } else if (event.type === \"done\") {\n const data = JSON.parse(event.data) as ChatDoneEvent;\n setSessionId(data.sessionId);\n setActiveTools([]);\n\n const updatedMessages: ChatMessage[] = data.messages.map((msg, i) => {\n if (data.impact && msg.role === \"assistant\" && i === data.messages.length - 1) {\n return { ...msg, impact: data.impact };\n }\n return msg;\n });\n\n // Preserve impact from prior messages\n const finalMessages = updatedMessages.map((msg, i) => {\n const prev = baseMessages[i];\n if (prev?.impact && !msg.impact) return { ...msg, impact: prev.impact };\n return msg;\n });\n\n setMessages(finalMessages);\n\n if (data.impact) {\n const emUg = data.impact.emissions?.total ?? 0;\n const enWms = data.impact.energy?.total ?? 0;\n const tMs = data.impact.inferenceTime?.total ?? 0;\n\n setSessionImpact((prev) => ({\n totalEmissionsUg: prev.totalEmissionsUg + emUg,\n totalEnergyWms: prev.totalEnergyWms + enWms,\n totalTimeMs: prev.totalTimeMs + tMs,\n messageCount: prev.messageCount + 1,\n }));\n\n setGlobalStats((prev) =>\n prev\n ? {\n totalMessages: prev.totalMessages + 1,\n totalEmissionsUg: prev.totalEmissionsUg + emUg,\n totalEnergyWms: prev.totalEnergyWms + enWms,\n totalTimeMs: prev.totalTimeMs + tMs,\n }\n : { totalMessages: 1, totalEmissionsUg: emUg, totalEnergyWms: enWms, totalTimeMs: tMs },\n );\n }\n } else if (event.type === \"error\") {\n const data = JSON.parse(event.data) as ChatErrorEvent;\n throw new Error(data.error);\n }\n // Ignore unknown event types (including intermediate approval_request)\n }\n }\n }\n\n async function sendMessage(nextMessage: string) {\n const trimmedInput = nextMessage.trim();\n if (!trimmedInput || isLoading || eqLoading) return;\n\n // Route through EQ flow if active\n if (eqFlowState && eqFlowState.step !== \"done\") {\n handleEQResponse(trimmedInput);\n return;\n }\n\n const nextMessages: ChatMessage[] = [...messages, { role: \"user\", content: trimmedInput }];\n setMessages(nextMessages);\n setInput(\"\");\n setIsLoading(true);\n setErrorMessage(null);\n setActiveTools([]);\n\n try {\n const response = await fetch(\"/api/chat\", {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({\n sessionId,\n messages: nextMessages.map(({ role, content }) => ({ role, content })),\n }),\n });\n\n if (!response.ok) {\n const errorBody = (await response.json()) as { error?: string };\n throw new Error(errorBody.error ?? \"Failed to send chat message\");\n }\n\n await handleSSEStream(response, nextMessages);\n } catch (error) {\n const message = error instanceof Error ? error.message : \"Failed to send chat message\";\n setErrorMessage(message);\n } finally {\n setIsLoading(false);\n setActiveTools([]);\n }\n }\n\n async function handleApproval(approved: boolean) {\n if (!pendingApproval) return;\n\n const approval = pendingApproval;\n // Use the messages from the approval event (these are the ChatMessage[] at the point of approval)\n const replayMessages: ChatMessage[] = approval.messages.map((m) => ({ role: m.role, content: m.content }));\n // Also restore impact from our local state\n const baseMessages = replayMessages.map((msg, i) => {\n const existing = messages[i];\n if (existing?.impact) return { ...msg, impact: existing.impact };\n return msg;\n });\n\n setPendingApproval(null);\n setIsLoading(true);\n setErrorMessage(null);\n setActiveTools([]);\n\n try {\n const response = await fetch(\"/api/chat/approve\", {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({\n sessionId: approval.sessionId,\n messages: replayMessages.map(({ role, content }) => ({ role, content })),\n approved,\n pendingToolCalls: approval.toolCalls,\n }),\n });\n\n if (!response.ok) {\n const errorBody = (await response.json()) as { error?: string };\n throw new Error(errorBody.error ?? \"Failed to process approval\");\n }\n\n await handleSSEStream(response, baseMessages);\n } catch (error) {\n const message = error instanceof Error ? error.message : \"Failed to process approval\";\n setErrorMessage(message);\n } finally {\n setIsLoading(false);\n setActiveTools([]);\n }\n }\n\n function onSubmit(event: FormEvent) {\n event.preventDefault();\n sendMessage(input);\n }\n\n function onKeyDown(event: KeyboardEvent) {\n if (event.key === \"Enter\" && !event.shiftKey && canSend) {\n event.preventDefault();\n sendMessage(input);\n }\n }\n\n return (\n

\n \n

\n {!hasStartedChat && (\n

Ask your local AI

\n Start a conversation with our eco-friendly AI assistant.
\n Ask questions, get help, or just chat!\n

\n {activeEQ?.active && (\n \n Energy Question of the Week\n \n \n )}\n

Try Asking

\n {STARTER_PROMPTS.map((prompt) => (\n \n ))}\n

\n )}\n\n {messages.map((message, index) => {\n const isStreamingMsg = isLoading && message.role === \"assistant\" && index === messages.length - 1;\n\n return (\n

{message.role === \"assistant\" ? \"AI Assistant\" : \"You\"}

\n {message.content}\n

\n {message.role === \"assistant\" && message.impact && }\n

\n );\n })}\n\n {isLoading && activeTools.some((t) => t.status === \"running\") && }\n\n {pendingApproval && (\n handleApproval(true)}\n onReject={() => handleApproval(false)}\n />\n )}\n\n {isLoading && !pendingApproval && !activeTools.some((t) => t.status === \"running\") && (\n

Assistant is thinking...

\n )}\n {errorMessage &&

{errorMessage}

}\n

\n\n {hasStartedChat && (\n

\n \n \n

\n )}\n\n \n

\n );\n}\n",[],[201,364,355],"components","aa56e478-0cb6-11f1-8b14-42dde27851f2",["D",1771411410564],[274],[201,364],["D",1771411410564],[273,275,276,277,278],[201],"b01d1fd8-1712-11f1-84b7-42dde27851f2",["D",1772550445658],[],[206,32],["D",1772550445410],[271],[206],"6b6ec67c-1701-11f1-a0fe-42dde27851f2","weekly-questions.ts","bf5742a6-1656-11f1-b17f-42dde27851f2",140,["D",1772543028990],"export interface WeeklyQuestion {\n week: number;\n publishDate: string;\n active_from: string;\n active_until: string;\n question: string;\n answer: {\n value: number;\n unit: string;\n secondary?: string;\n };\n explanation: string;\n sources: string[];\n nextWeekTeaser: string;\n meta?: Record;\n}\n\nexport function getActiveQuestion(now: Date = new Date()): WeeklyQuestion | null {\n const ts = now.getTime();\n return (\n WEEKLY_QUESTIONS.find(\n (q) => ts >= new Date(q.active_from).getTime() && ts < new Date(q.active_until).getTime(),\n ) ?? null\n );\n}\n\nexport const WEEKLY_QUESTIONS: WeeklyQuestion[] = [\n {\n week: 1,\n publishDate: \"2026-03-02\",\n active_from: \"2026-03-02T00:00:00Z\",\n active_until: \"2026-03-09T00:00:00Z\",\n question:\n \"How much CO\\u2082 would we save per month by running the office dishwasher at night (2\\u20136 AM) instead of the evening (6\\u201310 PM)?\",\n answer: {\n value: 2.9,\n unit: \"kgCO\\u2082/month\",\n secondary: \"\\u20AC2.64/month saved on electricity\",\n },\n explanation: `**About 2.9 kg of CO\\u2082 per month** \\u2014 and roughly \\u20AC2.64 off the electricity bill.\n\nThat might sound modest for a single appliance, but here\\u2019s why it matters. At night, Dutch electricity is greener: wind turbines keep spinning while demand drops, so the grid runs on about 250\\u2013300 g CO\\u2082 per kWh. During the evening peak (6\\u201310 PM), gas-fired plants ramp up to cover demand just as solar output fades, pushing the grid to 400\\u2013450 g CO\\u2082 per kWh. By shifting one ~1 kWh dishwasher cycle to the early morning, each run avoids roughly 130 g of CO\\u2082. Over 22 workdays that\\u2019s almost 3 kg per month \\u2014 about 35 kg per year.\n\nThe cost story is similar. Off-peak rates in the Netherlands sit around \\u20AC0.15\\u20130.18/kWh compared to \\u20AC0.28\\u20130.35/kWh in the evening, saving about \\u20AC0.12 per cycle. It won\\u2019t pay for a holiday, but multiply the same logic across every deferrable load in the building \\u2014 water heaters, EV chargers, laundry \\u2014 and the numbers start to add up. The easiest kilogram of CO\\u2082 to cut is the one you shift, not the one you skip.`,\n sources: [\n \"EU energy labelling regulation 2019/2022\",\n \"Dutch dynamic tariff data (gridio.io)\",\n \"Electricity Maps, Grid-in-Review 2025 Netherlands\",\n ],\n nextWeekTeaser:\n \"Next week: does it actually help the planet to unplug your monitor overnight, or is that just a myth from the CRT era?\",\n meta: {\n dishwasherKwhPerCycle: 1.0,\n workdaysPerMonth: 22,\n nightCO2gPerKwh: { min: 250, max: 300 },\n eveningCO2gPerKwh: { min: 400, max: 450 },\n co2DifferenceGPerKwh: 130,\n nightRateEurPerKwh: { min: 0.15, max: 0.18 },\n eveningRateEurPerKwh: { min: 0.28, max: 0.35 },\n rateDifferenceEurPerKwh: 0.12,\n },\n },\n];\n",[],[212,380],"9d388956-1312-11f1-9d55-42dde27851f2","prompts.ts","24a3046a-0d79-11f1-a265-42dde27851f2",133,["D",1772110609314],4,"export const STARTER_PROMPTS = [\n \"What time is it in the studio?\",\n \"Which room is available now?\",\n \"I have an idea for lunch.\",\n];\n",[],[212,388],"c167a64e-1656-11f1-bcc8-42dde27851f2","2472dac4-0d79-11f1-93af-42dde27851f2",138,["D",1772469729283],"# Data Configuration\n\n## Starter Prompts\n\nThe starter prompts displayed to the user before a chat begins are configured in `prompts.ts`.\n\nTo update the prompts:\n1. Open `data/prompts.ts`.\n2. Modify the `STARTER_PROMPTS` array.\n3. Add, remove, or edit the strings as needed.\n\nExample:\n```typescript\nexport const STARTER_PROMPTS = [\n \"New prompt 1\",\n \"New prompt 2\",\n];\n```\n\n## Weekly Questions\n\nThe weekly sustainability questions are configured in `weekly-questions.ts`.\n\nEach entry in the `WEEKLY_QUESTIONS` array contains:\n- `week` / `publishDate` — scheduling info\n- `question` — the question shown to users\n- `answer` — structured answer with `value`, `unit`, and optional `secondary` text\n- `explanation` — markdown explanation (2–3 paragraphs)\n- `sources` — list of references\n- `nextWeekTeaser` — teaser for the following week's question\n- `meta` (optional) — raw numbers behind the calculations\n\nTo add a new weekly question, append an entry to the array in `data/weekly-questions.ts`.\n\n## Assistant System Prompt\n\nThe backend assistant system prompt is managed in Langfuse Prompt Management.\n\n- Prompt name is configured with `LANGFUSE_SYSTEM_PROMPT_NAME`.\n- Optionally fetch by label with `LANGFUSE_SYSTEM_PROMPT_LABEL`.\n- Optionally pin a specific version via `LANGFUSE_SYSTEM_PROMPT_VERSION`.\n- If Langfuse prompt retrieval fails, the backend falls back to a local default system prompt so chat still works.\n",[],[212,167],["D",1771494937646],[267,268,269],[212],"201ca748-1337-11f1-8f62-42dde27851f2","tools.ts","b10349b2-1254-11f1-bc96-42dde27851f2",134,["D",1772126290791],"import type OpenAI from \"npm:openai\";\n\n// ── Tool definition ───────────────────────────────────────────────────\n\nexport interface ToolDefinition {\n name: string;\n description: string;\n parameters: Record;\n requiresApproval: boolean;\n execute: (args: Record) => Promise;\n}\n\n// ── Tool implementations ──────────────────────────────────────────────\n\nconst getCurrentTime: ToolDefinition = {\n name: \"get_current_time\",\n description:\n \"Returns the current date and time in ISO 8601 format. Use when the user asks about the current time or date.\",\n parameters: { type: \"object\", properties: {}, required: [] },\n requiresApproval: false,\n execute: async () => new Date().toISOString(),\n};\n\nconst calculateCarbonComparison: ToolDefinition = {\n name: \"calculate_carbon_comparison\",\n description:\n \"Compares CO2 emissions (in micrograms) of an AI query to everyday activities. Provide the emissions value.\",\n parameters: {\n type: \"object\",\n properties: {\n emissions_ug: {\n type: \"number\",\n description: \"CO2 emissions in micrograms to compare\",\n },\n },\n required: [\"emissions_ug\"],\n },\n requiresApproval: false,\n execute: async (args) => {\n const ug = Number(args.emissions_ug) || 0;\n const comparisons = [\n { activity: \"a single human breath\", ugCO2: 40_000 },\n { activity: \"boiling water for one cup of tea\", ugCO2: 15_000_000 },\n { activity: \"charging a smartphone\", ugCO2: 8_000_000 },\n { activity: \"driving a car for 1 km\", ugCO2: 120_000_000 },\n { activity: \"a Google search\", ugCO2: 200_000 },\n { activity: \"sending an email\", ugCO2: 4_000_000 },\n { activity: \"streaming 1 minute of video\", ugCO2: 3_600_000 },\n ];\n const lines = comparisons.map((c) => {\n const ratio = ug / c.ugCO2;\n if (ratio < 0.01) return `- ${(ratio * 100).toFixed(2)}% of ${c.activity}`;\n if (ratio < 1) return `- ${(ratio * 100).toFixed(1)}% of ${c.activity}`;\n return `- ${ratio.toFixed(1)}x ${c.activity}`;\n });\n return `CO2 comparison for ${ug} \\u00b5g CO\\u2082e:\\n${lines.join(\"\\n\")}`;\n },\n};\n\nconst webSearch: ToolDefinition = {\n name: \"web_search\",\n description:\n \"Search the web for current information. Use when the user asks about recent events or data you don't have.\",\n parameters: {\n type: \"object\",\n properties: {\n query: { type: \"string\", description: \"The search query\" },\n },\n required: [\"query\"],\n },\n requiresApproval: true,\n execute: async (args) => {\n // Placeholder for v1\n return `Web search for \"${args.query}\" is not yet available. This feature is coming soon. Please answer based on your training data instead.`;\n },\n};\n\n// ── Feedback notifier (swap for Slack, webhook, etc.) ────────────────\n\ninterface FeedbackPayload {\n rating: number;\n summary: string;\n comment?: string;\n timestamp: string;\n}\n\ntype FeedbackNotifier = (payload: FeedbackPayload) => Promise;\n\nconst consoleFeedbackNotifier: FeedbackNotifier = async (payload) => {\n console.log(\"[feedback]\", JSON.stringify(payload));\n};\n\nconst slackFeedbackNotifier: FeedbackNotifier = async (payload) => {\n const webhookUrl = Deno.env.get(\"FEEDBACK_SLACK_WEBHOOK_URL\")!;\n const stars = \"★\".repeat(payload.rating) + \"☆\".repeat(5 - payload.rating);\n const lines = [\n `*Conversation feedback* · ${stars} (${payload.rating}/5)`,\n `*Topic:* ${payload.summary}`,\n ...(payload.comment ? [`*Comment:* ${payload.comment}`] : []),\n `_${payload.timestamp}_`,\n ];\n const body = JSON.stringify({ text: lines.join(\"\\n\") });\n const res = await fetch(webhookUrl, {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body,\n });\n if (!res.ok) {\n console.error(\"[feedback] Slack POST failed\", res.status, await res.text());\n }\n};\n\nconst feedbackNotifier: FeedbackNotifier =\n Deno.env.get(\"FEEDBACK_SLACK_WEBHOOK_URL\")\n ? slackFeedbackNotifier\n : consoleFeedbackNotifier;\n\nconst FEEDBACK_TOOL_ENABLED = Deno.env.get(\"FEEDBACK_TOOL_ENABLED\") === \"true\";\n\nconst collectFeedback: ToolDefinition = {\n name: \"collect_feedback\",\n description:\n \"Log a quality assessment of the current interaction. \" +\n \"Call this AFTER you have finished answering a substantive user question \" +\n \"(not for greetings, clarifications, or trivial exchanges). \" +\n \"This is silent observability — the user does not see the output.\",\n parameters: {\n type: \"object\",\n properties: {\n rating: {\n type: \"integer\",\n minimum: 1,\n maximum: 5,\n description:\n \"Quality rating from 1 (poor) to 5 (excellent). Consider whether the user's question was fully answered, tools used effectively, and overall helpfulness.\",\n },\n summary: {\n type: \"string\",\n description: \"One-sentence summary of the topic or question addressed.\",\n },\n comment: {\n type: \"string\",\n description: \"Optional observation about what went well or could improve.\",\n },\n },\n required: [\"rating\", \"summary\"],\n },\n requiresApproval: false,\n execute: async (args) => {\n const payload: FeedbackPayload = {\n rating: Number(args.rating) || 3,\n summary: String(args.summary ?? \"\"),\n comment: args.comment ? String(args.comment) : undefined,\n timestamp: new Date().toISOString(),\n };\n await feedbackNotifier(payload);\n return \"Feedback recorded.\";\n },\n};\n\n// ── Lunch order tool ──────────────────────────────────────────────────\n\nconst LUNCH_WEBHOOK_ENABLED = Boolean(Deno.env.get(\"SLACK_LUNCH_WEBHOOK_URL\"));\n\nconst sendSlackLunchOrder: ToolDefinition = {\n name: \"send_slack_lunch_order\",\n description:\n \"Posts a lunch order to the office Slack channel. \" +\n \"Call this once you have collected the user's name, what they'd like to order, and any dietary notes.\",\n parameters: {\n type: \"object\",\n properties: {\n name: {\n type: \"string\",\n description: \"Full name of the person ordering\",\n },\n order: {\n type: \"string\",\n description: \"What the person would like to order\",\n },\n dietary_notes: {\n type: \"string\",\n description: \"Any dietary restrictions or special notes (optional)\",\n },\n },\n required: [\"name\", \"order\"],\n },\n requiresApproval: true,\n execute: async (args) => {\n const webhookUrl = Deno.env.get(\"SLACK_LUNCH_WEBHOOK_URL\");\n if (!webhookUrl) {\n return \"Lunch orders are not configured (SLACK_LUNCH_WEBHOOK_URL is not set).\";\n }\n\n const name = String(args.name);\n const order = String(args.order);\n const dietaryNotes = args.dietary_notes ? String(args.dietary_notes) : null;\n const timestamp = new Date().toLocaleString(\"en-GB\", {\n dateStyle: \"medium\",\n timeStyle: \"short\",\n });\n\n const blocks = [\n {\n type: \"header\",\n text: { type: \"plain_text\", text: \"🥗 Lunch Order\", emoji: true },\n },\n {\n type: \"section\",\n fields: [\n { type: \"mrkdwn\", text: `*Name*\\n${name}` },\n { type: \"mrkdwn\", text: `*Order*\\n${order}` },\n ...(dietaryNotes\n ? [{ type: \"mrkdwn\", text: `*Dietary notes*\\n${dietaryNotes}` }]\n : []),\n ],\n },\n {\n type: \"context\",\n elements: [{ type: \"mrkdwn\", text: `Submitted ${timestamp}` }],\n },\n ];\n\n const res = await fetch(webhookUrl, {\n method: \"POST\",\n headers: { \"content-type\": \"application/json\" },\n body: JSON.stringify({ blocks }),\n });\n\n if (!res.ok) {\n const detail = await res.text();\n console.error(\"[lunch] Slack POST failed\", res.status, detail);\n return `Failed to post lunch order (HTTP ${res.status}).`;\n }\n\n return `Lunch order for ${name} posted to Slack.`;\n },\n};\n\n// ── Exports ───────────────────────────────────────────────────────────\n\nexport const ALL_TOOLS: ToolDefinition[] = [\n getCurrentTime,\n calculateCarbonComparison,\n webSearch,\n ...(LUNCH_WEBHOOK_ENABLED ? [sendSlackLunchOrder] : []),\n ...(FEEDBACK_TOOL_ENABLED ? [collectFeedback] : []),\n];\n\nexport const TOOL_MAP: Record = Object.fromEntries(\n ALL_TOOLS.map((t) => [t.name, t]),\n);\n\nexport const TOOLS_REQUIRING_APPROVAL = new Set(\n ALL_TOOLS.filter((t) => t.requiresApproval).map((t) => t.name),\n);\n\nexport function getOpenAIToolDefinitions(): OpenAI.ChatCompletionTool[] {\n return ALL_TOOLS.map((t) => ({\n type: \"function\" as const,\n function: {\n name: t.name,\n description: t.description,\n parameters: t.parameters,\n },\n }));\n}\n",[],[218,407],"668b11b6-1705-11f1-8ad6-42dde27851f2","langgraph.ts","906ab7a0-0cb7-11f1-908d-42dde27851f2",158,["D",1772544738771],24,"import OpenAI from \"npm:openai\";\nimport { Langfuse } from \"npm:langfuse\";\nimport { getLangfuseCredentials } from \"./langfuse.ts\";\ntype LangfuseTraceClient = ReturnType;\nimport { StateGraph, START, END } from \"npm:@langchain/langgraph\";\nimport { AIMessage, ToolMessage } from \"npm:@langchain/core/messages\";\nimport type { RunnableConfig } from \"npm:@langchain/core/runnables\";\n\nimport {\n GraphState,\n type GraphStateType,\n type ImpactData,\n type ChatMessage,\n type ChatResult,\n type StreamEventHandler,\n type ToolCallInfo,\n type ApprovalRequest,\n chatMessagesToBaseMessages,\n baseMessagesToOpenAI,\n} from \"./graph-state.ts\";\n\nimport {\n TOOL_MAP,\n TOOLS_REQUIRING_APPROVAL,\n getOpenAIToolDefinitions,\n} from \"./tools.ts\";\n\n// Re-export types used by index.ts\nexport type { ChatMessage, ChatResult, ImpactData, StreamEventHandler, ToolCallInfo };\n\n// ── Config ────────────────────────────────────────────────────────────\n\nconst OPENAI_MODEL = Deno.env.get(\"OPENAI_MODEL\") ?? \"gpt-4o-mini\";\nconst OPENAI_BASE_URL = Deno.env.get(\"OPENAI_BASE_URL\");\nconst POST_PROCESS_CHAR_THRESHOLD = 800;\nconst FALLBACK_SYSTEM_PROMPT =\n \"You are a clear, practical assistant that helps users make informed choices about AI usage, privacy, and environmental impact. Be concise, factual, and transparent about uncertainty.\";\n\n// ── Environment helpers ───────────────────────────────────────────────\n\nfunction requireEnv(name: string): string {\n const value = Deno.env.get(name);\n if (!value) throw new Error(`Missing required environment variable: ${name}`);\n return value;\n}\n\n// ── Langfuse prompt resolution ────────────────────────────────────────\n\ninterface ResolvedSystemPrompt {\n content: string;\n source: \"langfuse\" | \"fallback\";\n}\n\nfunction parsePromptVersion(value: string | undefined): number | undefined {\n if (!value) return undefined;\n const parsed = Number.parseInt(value, 10);\n return Number.isFinite(parsed) ? parsed : undefined;\n}\n\nfunction extractPromptContent(prompt: unknown): string {\n if (typeof prompt === \"string\") return prompt;\n if (!prompt || typeof prompt !== \"object\") return \"\";\n\n const candidate = prompt as Record;\n if (typeof candidate.prompt === \"string\") return candidate.prompt;\n if (typeof candidate.content === \"string\") return candidate.content;\n if (typeof candidate.text === \"string\") return candidate.text;\n if (Array.isArray(candidate.prompt)) {\n return candidate.prompt\n .filter((item) => item && typeof item === \"object\")\n .map((item) => item as Record)\n .filter((item) => item.role === \"system\" && typeof item.content === \"string\")\n .map((item) => item.content as string)\n .join(\"\\n\")\n .trim();\n }\n return \"\";\n}\n\nasync function resolveSystemPrompt(langfuse: Langfuse): Promise {\n const promptName = Deno.env.get(\"LANGFUSE_SYSTEM_PROMPT_NAME\");\n if (!promptName) return { content: FALLBACK_SYSTEM_PROMPT, source: \"fallback\" };\n\n const promptLabel = Deno.env.get(\"LANGFUSE_SYSTEM_PROMPT_LABEL\");\n const configuredVersion = parsePromptVersion(Deno.env.get(\"LANGFUSE_SYSTEM_PROMPT_VERSION\"));\n\n try {\n const prompt =\n configuredVersion !== undefined\n ? await langfuse.getPrompt(promptName, configuredVersion)\n : promptLabel\n ? await langfuse.getPrompt(promptName, undefined, { label: promptLabel })\n : await langfuse.getPrompt(promptName);\n\n const content = extractPromptContent(prompt).trim();\n if (content.length > 0) return { content, source: \"langfuse\" };\n } catch {\n // Fall through to fallback\n }\n return { content: FALLBACK_SYSTEM_PROMPT, source: \"fallback\" };\n}\n\n// ── OpenAI client ─────────────────────────────────────────────────────\n\nfunction getOpenAIClient(): OpenAI {\n return new OpenAI({\n apiKey: requireEnv(\"OPENAI_API_KEY\"),\n baseURL: OPENAI_BASE_URL || undefined,\n });\n}\n\n// ── Graph nodes ───────────────────────────────────────────────────────\n\nasync function chatNode(\n state: typeof GraphState.State,\n config: RunnableConfig,\n): Promise> {\n const onEvent = config.configurable?.onEvent as StreamEventHandler | undefined;\n const trace = config.configurable?.langfuseTrace as LangfuseTraceClient | undefined;\n\n if (onEvent) await onEvent({ type: \"thinking\" });\n\n const client = getOpenAIClient();\n const openAIMessages = baseMessagesToOpenAI(state.messages);\n const tools = getOpenAIToolDefinitions();\n\n const nodeSpan = trace?.span({ name: \"chatNode\" });\n const generation = nodeSpan?.generation({\n name: \"openai-chat-completion\",\n model: OPENAI_MODEL,\n input: openAIMessages,\n metadata: { tools: tools.length > 0 ? tools : undefined },\n });\n\n const stream = await client.chat.completions.create({\n model: OPENAI_MODEL,\n messages: openAIMessages,\n tools: tools.length > 0 ? tools : undefined,\n stream: true,\n stream_options: { include_usage: true },\n });\n\n let content = \"\";\n let impact: ImpactData | undefined;\n let finishReason: string | undefined;\n let usage: { input: number; output: number } | undefined;\n const toolCallsAccum: Map = new Map();\n\n for await (const chunk of stream) {\n const choice = chunk.choices?.[0];\n const delta = choice?.delta;\n\n if (choice?.finish_reason) {\n finishReason = choice.finish_reason;\n }\n\n if (chunk.usage) {\n usage = { input: chunk.usage.prompt_tokens, output: chunk.usage.completion_tokens };\n }\n\n if (delta?.content) {\n content += delta.content;\n if (onEvent) await onEvent({ type: \"chunk\", delta: delta.content });\n }\n\n if (delta?.tool_calls) {\n for (const tc of delta.tool_calls) {\n const existing = toolCallsAccum.get(tc.index);\n if (existing) {\n if (tc.function?.arguments) existing.arguments += tc.function.arguments;\n } else {\n toolCallsAccum.set(tc.index, {\n id: tc.id || \"\",\n name: tc.function?.name || \"\",\n arguments: tc.function?.arguments || \"\",\n });\n }\n }\n }\n\n const rawImpact = (chunk as Record).impact;\n if (rawImpact && typeof rawImpact === \"object\") {\n impact = rawImpact as ImpactData;\n }\n }\n\n const toolCalls = Array.from(toolCallsAccum.values()).map((tc) => ({\n id: tc.id,\n name: tc.name,\n args: JSON.parse(tc.arguments || \"{}\"),\n type: \"tool_call\" as const,\n }));\n\n generation?.end({\n output: { content, tool_calls: toolCalls.length > 0 ? toolCalls : undefined, finish_reason: finishReason },\n usage: usage ? { input: usage.input, output: usage.output } : undefined,\n });\n nodeSpan?.end();\n\n const aiMessage = new AIMessage({\n content: content || \"\",\n tool_calls: toolCalls.length > 0 ? toolCalls : undefined,\n });\n\n const result: Partial = {\n messages: [aiMessage],\n };\n if (content) result.assistantMessage = content;\n if (impact) result.impact = impact;\n\n return result;\n}\n\nasync function toolExecutionNode(\n state: typeof GraphState.State,\n config: RunnableConfig,\n): Promise> {\n const onEvent = config.configurable?.onEvent as StreamEventHandler | undefined;\n const trace = config.configurable?.langfuseTrace as LangfuseTraceClient | undefined;\n const lastMessage = state.messages[state.messages.length - 1];\n\n if (!(lastMessage instanceof AIMessage) || !lastMessage.tool_calls?.length) {\n return { messages: [] };\n }\n\n const nodeSpan = trace?.span({ name: \"toolExecutionNode\" });\n const toolMessages: ToolMessage[] = [];\n\n for (const toolCall of lastMessage.tool_calls) {\n const toolDef = TOOL_MAP[toolCall.name];\n if (!toolDef) {\n toolMessages.push(\n new ToolMessage({ content: `Error: Unknown tool \"${toolCall.name}\"`, tool_call_id: toolCall.id ?? \"\" }),\n );\n continue;\n }\n\n if (onEvent) await onEvent({ type: \"tool_start\", toolName: toolCall.name, toolCallId: toolCall.id ?? \"\" });\n\n const toolSpan = nodeSpan?.span({ name: `tool:${toolCall.name}`, input: toolCall.args });\n\n try {\n const result = await toolDef.execute(toolCall.args as Record);\n toolMessages.push(new ToolMessage({ content: result, tool_call_id: toolCall.id ?? \"\" }));\n toolSpan?.end({ output: result });\n if (onEvent) await onEvent({ type: \"tool_result\", toolName: toolCall.name, toolCallId: toolCall.id ?? \"\", result });\n } catch (error) {\n const errorMsg = error instanceof Error ? error.message : \"Tool execution failed\";\n toolMessages.push(new ToolMessage({ content: `Error: ${errorMsg}`, tool_call_id: toolCall.id ?? \"\" }));\n toolSpan?.end({ output: `Error: ${errorMsg}`, level: \"ERROR\" });\n if (onEvent) await onEvent({ type: \"tool_result\", toolName: toolCall.name, toolCallId: toolCall.id ?? \"\", result: `Error: ${errorMsg}` });\n }\n }\n\n nodeSpan?.end();\n return { messages: toolMessages };\n}\n\nasync function approvalNode(\n state: typeof GraphState.State,\n config: RunnableConfig,\n): Promise> {\n const onEvent = config.configurable?.onEvent as StreamEventHandler | undefined;\n const trace = config.configurable?.langfuseTrace as LangfuseTraceClient | undefined;\n const lastMessage = state.messages[state.messages.length - 1];\n\n if (!(lastMessage instanceof AIMessage) || !lastMessage.tool_calls?.length) {\n return {};\n }\n\n const toolCalls: ToolCallInfo[] = lastMessage.tool_calls.map((tc) => ({\n id: tc.id ?? \"\",\n name: tc.name,\n arguments: tc.args as Record,\n }));\n\n const approvalSpan = trace?.span({\n name: \"approvalNode\",\n input: { toolNames: toolCalls.map((tc) => tc.name) },\n });\n\n if (onEvent) await onEvent({ type: \"approval_request\", toolCalls });\n\n approvalSpan?.end({ output: { toolCalls } });\n\n return { approvalRequest: { toolCalls } };\n}\n\nasync function postProcessNode(\n state: typeof GraphState.State,\n config: RunnableConfig,\n): Promise> {\n const onEvent = config.configurable?.onEvent as StreamEventHandler | undefined;\n const trace = config.configurable?.langfuseTrace as LangfuseTraceClient | undefined;\n const original = state.assistantMessage;\n\n if (!original || original.length < POST_PROCESS_CHAR_THRESHOLD) {\n return {};\n }\n\n if (onEvent) await onEvent({ type: \"post_process_start\" });\n\n const nodeSpan = trace?.span({ name: \"postProcessNode\" });\n\n const postProcessMessages = [\n {\n role: \"system\" as const,\n content:\n \"You are a conciseness editor. Rewrite the following AI response to be more concise while preserving all key information and meaning. Remove filler words, redundant phrases, and unnecessary caveats. Keep the same tone and formatting (markdown). Output ONLY the rewritten response.\",\n },\n { role: \"user\" as const, content: original },\n ];\n\n const generation = nodeSpan?.generation({\n name: \"openai-post-process\",\n model: OPENAI_MODEL,\n input: postProcessMessages,\n });\n\n const client = getOpenAIClient();\n const stream = await client.chat.completions.create({\n model: OPENAI_MODEL,\n messages: postProcessMessages,\n stream: true,\n stream_options: { include_usage: true },\n });\n\n let refined = \"\";\n let impact: ImpactData | undefined;\n let finishReason: string | undefined;\n let usage: { input: number; output: number } | undefined;\n\n for await (const chunk of stream) {\n const choice = chunk.choices?.[0];\n const delta = choice?.delta?.content ?? \"\";\n\n if (choice?.finish_reason) {\n finishReason = choice.finish_reason;\n }\n\n if (chunk.usage) {\n usage = { input: chunk.usage.prompt_tokens, output: chunk.usage.completion_tokens };\n }\n\n if (delta) {\n refined += delta;\n if (onEvent) await onEvent({ type: \"chunk\", delta });\n }\n\n const rawImpact = (chunk as Record).impact;\n if (rawImpact && typeof rawImpact === \"object\") {\n impact = rawImpact as ImpactData;\n }\n }\n\n generation?.end({\n output: { content: refined, finish_reason: finishReason },\n usage: usage ? { input: usage.input, output: usage.output } : undefined,\n });\n nodeSpan?.end();\n\n const result: Partial = {};\n if (refined) result.assistantMessage = refined;\n if (impact) result.impact = impact;\n\n return result;\n}\n\n// ── Routing ───────────────────────────────────────────────────────────\n\nfunction routeAfterChat(state: typeof GraphState.State): string {\n const lastMessage = state.messages[state.messages.length - 1];\n\n if (lastMessage instanceof AIMessage && lastMessage.tool_calls?.length) {\n const needsApproval = lastMessage.tool_calls.some((tc) =>\n TOOLS_REQUIRING_APPROVAL.has(tc.name),\n );\n return needsApproval ? \"approval\" : \"tools\";\n }\n\n if (state.assistantMessage && state.assistantMessage.length >= POST_PROCESS_CHAR_THRESHOLD) {\n return \"postProcess\";\n }\n\n return END;\n}\n\n// ── Graph builder ─────────────────────────────────────────────────────\n\nfunction buildGraph() {\n const workflow = new StateGraph(GraphState)\n .addNode(\"chat\", chatNode)\n .addNode(\"tools\", toolExecutionNode)\n .addNode(\"approval\", approvalNode)\n .addNode(\"postProcess\", postProcessNode)\n .addEdge(START, \"chat\")\n .addConditionalEdges(\"chat\", routeAfterChat)\n .addEdge(\"tools\", \"chat\")\n .addEdge(\"approval\", END)\n .addEdge(\"postProcess\", END);\n\n return workflow.compile();\n}\n\n// ── Main entry point ──────────────────────────────────────────────────\n\nexport async function runChatGraphStream(\n messages: ChatMessage[],\n sessionId: string,\n onEvent?: StreamEventHandler,\n): Promise {\n const langfuse = new Langfuse(getLangfuseCredentials());\n const resolvedSystemPrompt = await resolveSystemPrompt(langfuse);\n console.log(`[system-prompt] source=${resolvedSystemPrompt.source}, length=${resolvedSystemPrompt.content.length}`);\n\n const trace = langfuse.trace({\n sessionId,\n input: { systemPrompt: resolvedSystemPrompt.content, messages },\n metadata: { runtime: \"val-town\", promptSource: resolvedSystemPrompt.source },\n });\n\n const baseMessages = chatMessagesToBaseMessages(messages, resolvedSystemPrompt.content);\n const graph = buildGraph();\n\n try {\n const result = await graph.invoke(\n {\n messages: baseMessages,\n sessionId,\n assistantMessage: \"\",\n impact: undefined,\n approvalRequest: undefined,\n },\n { configurable: { onEvent, langfuseTrace: trace } },\n );\n\n const finalMessage = result.assistantMessage;\n\n if (!finalMessage?.trim() && !result.approvalRequest) {\n throw new Error(\"API response did not include assistant text output\");\n }\n\n trace.update({ output: finalMessage });\n\n return {\n assistantMessage: finalMessage,\n messages: [...messages, ...(finalMessage ? [{ role: \"assistant\" as const, content: finalMessage }] : [])],\n impact: result.impact,\n approvalRequest: result.approvalRequest,\n };\n } finally {\n await langfuse.flushAsync();\n }\n}\n\n// ── Approval flow entry point ─────────────────────────────────────────\n\nexport async function runChatGraphWithApproval(\n messages: ChatMessage[],\n sessionId: string,\n approved: boolean,\n pendingToolCalls: ToolCallInfo[],\n onEvent?: StreamEventHandler,\n): Promise {\n const langfuse = new Langfuse(getLangfuseCredentials());\n const resolvedSystemPrompt = await resolveSystemPrompt(langfuse);\n\n const trace = langfuse.trace({\n sessionId,\n input: { systemPrompt: resolvedSystemPrompt.content, messages, approved },\n metadata: { runtime: \"val-town\", promptSource: resolvedSystemPrompt.source, approvalFlow: true },\n });\n\n const baseMessages = chatMessagesToBaseMessages(messages, resolvedSystemPrompt.content);\n\n try {\n if (approved) {\n // Re-add the AI tool-call message, execute tools, then resume the graph\n const aiMessage = new AIMessage({\n content: \"\",\n tool_calls: pendingToolCalls.map((tc) => ({\n id: tc.id,\n name: tc.name,\n args: tc.arguments,\n type: \"tool_call\" as const,\n })),\n });\n\n // Execute the approved tools with Langfuse spans\n const toolExecSpan = trace.span({ name: \"toolExecutionNode\" });\n const toolMessages: ToolMessage[] = [];\n for (const tc of pendingToolCalls) {\n const toolDef = TOOL_MAP[tc.name];\n if (!toolDef) continue;\n\n if (onEvent) await onEvent({ type: \"tool_start\", toolName: tc.name, toolCallId: tc.id });\n\n const toolSpan = toolExecSpan.span({ name: `tool:${tc.name}`, input: tc.arguments });\n\n try {\n const result = await toolDef.execute(tc.arguments);\n toolMessages.push(new ToolMessage({ content: result, tool_call_id: tc.id }));\n toolSpan.end({ output: result });\n if (onEvent) await onEvent({ type: \"tool_result\", toolName: tc.name, toolCallId: tc.id, result });\n } catch (error) {\n const errorMsg = error instanceof Error ? error.message : \"Tool execution failed\";\n toolMessages.push(new ToolMessage({ content: `Error: ${errorMsg}`, tool_call_id: tc.id }));\n toolSpan.end({ output: `Error: ${errorMsg}`, level: \"ERROR\" });\n if (onEvent) await onEvent({ type: \"tool_result\", toolName: tc.name, toolCallId: tc.id, result: `Error: ${errorMsg}` });\n }\n }\n toolExecSpan.end();\n\n // Run the graph with tool results already included\n const allMessages = [...baseMessages, aiMessage, ...toolMessages];\n const graph = buildGraph();\n\n const graphResult = await graph.invoke(\n { messages: allMessages, sessionId, assistantMessage: \"\", impact: undefined, approvalRequest: undefined },\n { configurable: { onEvent, langfuseTrace: trace } },\n );\n\n trace.update({ output: graphResult.assistantMessage });\n\n return {\n assistantMessage: graphResult.assistantMessage,\n messages: [...messages, { role: \"assistant\", content: graphResult.assistantMessage }],\n impact: graphResult.impact,\n };\n }\n\n // Rejected: ask the LLM to answer without tools\n const graph = buildGraph();\n const rejectionNote = new AIMessage({\n content: \"I wanted to use a tool to help answer, but the request was declined. Let me answer with my existing knowledge.\",\n });\n\n const graphResult = await graph.invoke(\n {\n messages: [...baseMessages, rejectionNote],\n sessionId,\n assistantMessage: \"\",\n impact: undefined,\n approvalRequest: undefined,\n },\n { configurable: { onEvent, langfuseTrace: trace } },\n );\n\n trace.update({ output: graphResult.assistantMessage });\n\n return {\n assistantMessage: graphResult.assistantMessage,\n messages: [...messages, { role: \"assistant\", content: graphResult.assistantMessage }],\n impact: graphResult.impact,\n };\n } finally {\n await langfuse.flushAsync();\n }\n}\n",[],[218,415],"5e0013ca-1705-11f1-b880-42dde27851f2","langfuse.ts","5e0014b0-1705-11f1-b880-42dde27851f2",156,["D",1772544724444],"import { Langfuse } from \"npm:langfuse\";\n\nfunction requireEnv(name: string): string {\n const value = Deno.env.get(name);\n if (!value) throw new Error(`Missing required environment variable: ${name}`);\n return value;\n}\n\nexport function getLangfuseCredentials() {\n return {\n publicKey: requireEnv(\"LANGFUSE_PUBLIC_KEY\"),\n secretKey: requireEnv(\"LANGFUSE_SECRET_KEY\"),\n baseUrl: Deno.env.get(\"LANGFUSE_BASE_URL\") ?? \"https://langfuse.greenpt.ai\",\n };\n}\n\nexport function createLangfuse(): Langfuse {\n return new Langfuse(getLangfuseCredentials());\n}\n",[],[218,424],"2a52c126-1255-11f1-8391-42dde27851f2","graph-state.ts","ab0550aa-1254-11f1-bda9-42dde27851f2",110,["D",1772029241640],"import { Annotation, END } from \"npm:@langchain/langgraph\";\nimport {\n BaseMessage,\n HumanMessage,\n AIMessage,\n SystemMessage,\n ToolMessage,\n} from \"npm:@langchain/core/messages\";\nimport type OpenAI from \"npm:openai\";\n\n// ── Shared types ──────────────────────────────────────────────────────\n\nexport type ChatRole = \"user\" | \"assistant\";\n\nexport interface ChatMessage {\n role: ChatRole;\n content: string;\n}\n\nexport interface ImpactData {\n version?: string;\n inferenceTime?: { total: number; unit: string };\n energy?: { total: number; unit: string };\n emissions?: { total: number; unit: string };\n}\n\nexport interface ToolCallInfo {\n id: string;\n name: string;\n arguments: Record;\n}\n\nexport interface ApprovalRequest {\n toolCalls: ToolCallInfo[];\n}\n\n// ── Stream events emitted by the graph ────────────────────────────────\n\nexport type GraphStreamEvent =\n | { type: \"chunk\"; delta: string }\n | { type: \"thinking\" }\n | { type: \"tool_start\"; toolName: string; toolCallId: string }\n | { type: \"tool_result\"; toolName: string; toolCallId: string; result: string }\n | { type: \"approval_request\"; toolCalls: ToolCallInfo[] }\n | { type: \"post_process_start\" };\n\nexport type StreamEventHandler = (event: GraphStreamEvent) => void | Promise;\n\n// ── Graph result ──────────────────────────────────────────────────────\n\nexport interface ChatResult {\n assistantMessage: string;\n messages: ChatMessage[];\n impact?: ImpactData;\n approvalRequest?: ApprovalRequest;\n}\n\n// ── Graph state (Annotation.Root) ─────────────────────────────────────\n\nexport const GraphState = Annotation.Root({\n messages: Annotation({\n reducer: (curr: BaseMessage[], update: BaseMessage[]) => [...curr, ...update],\n default: () => [],\n }),\n assistantMessage: Annotation({\n reducer: (_: string, update: string) => update,\n default: () => \"\",\n }),\n impact: Annotation({\n reducer: (_: ImpactData | undefined, update: ImpactData | undefined) => update,\n default: () => undefined,\n }),\n sessionId: Annotation({\n reducer: (_: string, update: string) => update,\n default: () => \"\",\n }),\n approvalRequest: Annotation({\n reducer: (_: ApprovalRequest | undefined, update: ApprovalRequest | undefined) => update,\n default: () => undefined,\n }),\n});\n\nexport type GraphStateType = typeof GraphState.State;\n\n// ── Message conversion: ChatMessage[] → BaseMessage[] ─────────────────\n\nexport function chatMessagesToBaseMessages(\n messages: ChatMessage[],\n systemPrompt: string,\n): BaseMessage[] {\n const result: BaseMessage[] = [new SystemMessage(systemPrompt)];\n for (const msg of messages) {\n if (msg.role === \"user\") {\n result.push(new HumanMessage(msg.content));\n } else {\n result.push(new AIMessage(msg.content));\n }\n }\n return result;\n}\n\n// ── Message conversion: BaseMessage[] → OpenAI format ─────────────────\n\nexport function baseMessagesToOpenAI(\n messages: BaseMessage[],\n): OpenAI.ChatCompletionMessageParam[] {\n return messages.map((msg) => {\n if (msg._getType() === \"system\") {\n return { role: \"system\" as const, content: msg.content as string };\n }\n if (msg._getType() === \"human\") {\n return { role: \"user\" as const, content: msg.content as string };\n }\n if (msg._getType() === \"ai\") {\n const aiMsg = msg as AIMessage;\n const param: OpenAI.ChatCompletionAssistantMessageParam = {\n role: \"assistant\" as const,\n content: (aiMsg.content as string) || null,\n };\n if (aiMsg.tool_calls && aiMsg.tool_calls.length > 0) {\n param.tool_calls = aiMsg.tool_calls.map((tc) => ({\n id: tc.id ?? \"\",\n type: \"function\" as const,\n function: {\n name: tc.name,\n arguments: JSON.stringify(tc.args),\n },\n }));\n }\n return param;\n }\n if (msg._getType() === \"tool\") {\n const toolMsg = msg as ToolMessage;\n return {\n role: \"tool\" as const,\n tool_call_id: toolMsg.tool_call_id,\n content: toolMsg.content as string,\n };\n }\n return { role: \"user\" as const, content: msg.content as string };\n });\n}\n\n// Re-export END for use in routing functions\nexport { END };\n",[],[218,432],"8e7d5f84-1701-11f1-9fb8-42dde27851f2","energy-question.ts","8e7d606a-1701-11f1-9fb8-42dde27851f2",144,["D",1772543087809],"import { getActiveQuestion } from \"../data/weekly-questions.ts\";\nimport { upsertSubmission } from \"./database/queries.ts\";\n\n// ── Types ──────────────────────────────────────────────────────────────\n\nexport type EQFlowStep = \"collect_answer\" | \"collect_email\" | \"done\";\n\nexport interface EQFlowState {\n step: EQFlowStep;\n week: number;\n questionText: string;\n rawAnswer?: string;\n email?: string;\n emailRetryCount?: number;\n}\n\ninterface ChatMessage {\n role: \"user\" | \"assistant\";\n content: string;\n}\n\nexport interface EQStepResult {\n state: EQFlowState;\n messages: ChatMessage[];\n}\n\n// ── Helpers ─────────────────────────────────────────────────────────────\n\nconst EMAIL_RE = /^[^\\s@]+@[^\\s@]+\\.[^\\s@]+$/;\nconst MAX_EMAIL_RETRIES = 2;\nconst REFUSAL_KEYWORDS = [\"no\", \"skip\", \"pass\", \"don't want\", \"no thanks\", \"nah\", \"nope\", \"rather not\"];\n\nfunction extractFirstName(email: string): string {\n const local = email.split(\"@\")[0].split(\".\")[0];\n return local.charAt(0).toUpperCase() + local.slice(1).toLowerCase();\n}\n\nfunction isRefusal(input: string): boolean {\n const lower = input.toLowerCase().trim();\n return REFUSAL_KEYWORDS.some((kw) => lower === kw || lower.startsWith(kw + \" \"));\n}\n\n// ── Flow functions ──────────────────────────────────────────────────────\n\nexport function startEnergyQuestion(): {\n active: true;\n state: EQFlowState;\n messages: ChatMessage[];\n} | { active: false } {\n const question = getActiveQuestion();\n if (!question) return { active: false };\n\n const state: EQFlowState = {\n step: \"collect_answer\",\n week: question.week,\n questionText: question.question,\n };\n\n return {\n active: true,\n state,\n messages: [\n {\n role: \"assistant\",\n content: `**Energy Question of the Week (Week ${question.week})**\\n\\n${question.question}\\n\\nType your best guess below!`,\n },\n ],\n };\n}\n\nexport function submitAnswer(flowState: EQFlowState, userInput: string): EQStepResult {\n const nextState: EQFlowState = {\n ...flowState,\n step: \"collect_email\",\n rawAnswer: userInput.trim(),\n emailRetryCount: 0,\n };\n\n return {\n state: nextState,\n messages: [\n {\n role: \"assistant\",\n content: `Got it — your guess is noted!\\n\\nTo save your answer, please share your email address. We'll send you the results when the answer is revealed.`,\n },\n ],\n };\n}\n\nexport async function submitEmail(\n flowState: EQFlowState,\n userInput: string,\n): Promise {\n const trimmed = userInput.trim();\n\n // Check for refusal\n if (isRefusal(trimmed)) {\n return {\n state: { ...flowState, step: \"done\" },\n messages: [\n {\n role: \"assistant\",\n content: `No problem! Your guess has been discarded. Feel free to ask me anything else.`,\n },\n ],\n };\n }\n\n // Validate email\n if (!EMAIL_RE.test(trimmed)) {\n const retryCount = (flowState.emailRetryCount ?? 0) + 1;\n if (retryCount >= MAX_EMAIL_RETRIES) {\n return {\n state: { ...flowState, step: \"done\" },\n messages: [\n {\n role: \"assistant\",\n content: `That doesn't look like a valid email. No worries — your guess wasn't saved, but you can still chat with me about anything!`,\n },\n ],\n };\n }\n return {\n state: { ...flowState, emailRetryCount: retryCount },\n messages: [\n {\n role: \"assistant\",\n content: `Hmm, that doesn't look like a valid email address. Could you try again? (Or type \"skip\" to skip.)`,\n },\n ],\n };\n }\n\n // Upsert submission\n const { isUpdate } = await upsertSubmission({\n email: trimmed,\n week: flowState.week,\n rawAnswer: flowState.rawAnswer!,\n });\n\n // Stub email\n sendConfirmationEmail(trimmed, flowState);\n\n const name = extractFirstName(trimmed);\n const updateNote = isUpdate ? \" (We've updated your previous answer.)\" : \"\";\n\n return {\n state: { ...flowState, step: \"done\", email: trimmed },\n messages: [\n {\n role: \"assistant\",\n content: `Thanks, ${name}! Your guess has been recorded.${updateNote}\\n\\nWe'll email you when the answer is revealed. In the meantime, feel free to ask me anything!`,\n },\n ],\n };\n}\n\nfunction sendConfirmationEmail(email: string, flowState: EQFlowState): void {\n console.log(\n `[EQ Stub] Would send confirmation email to ${email} for week ${flowState.week}, answer: \"${flowState.rawAnswer}\"`,\n );\n}\n",[],[218,440],"7d539b74-1701-11f1-8c77-42dde27851f2","queries.ts","2f146290-0e69-11f1-8f46-42dde27851f2","2ef968b4-0e69-11f1-a786-42dde27851f2",143,["D",1772543059002],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\n\nconst TABLE = \"greenpt_impact_v1\";\nconst EQ_SUBMISSIONS_TABLE = \"greenpt_eq_submissions_v1\";\n\nexport interface ImpactRecord {\n sessionId: string;\n emissionsUg: number;\n energyWms: number;\n inferenceTimeMs: number;\n}\n\nexport interface GlobalStats {\n totalMessages: number;\n totalEmissionsUg: number;\n totalEnergyWms: number;\n totalTimeMs: number;\n}\n\nexport async function insertImpact(record: ImpactRecord): Promise {\n await sqlite.execute({\n sql: `INSERT INTO ${TABLE} (session_id, emissions_ug, energy_wms, inference_time_ms) VALUES (?, ?, ?, ?)`,\n args: [record.sessionId, record.emissionsUg, record.energyWms, record.inferenceTimeMs],\n });\n}\n\nexport async function getGlobalStats(): Promise {\n const result = await sqlite.execute(\n `SELECT COUNT(*) as cnt, COALESCE(SUM(emissions_ug), 0) as emissions, COALESCE(SUM(energy_wms), 0) as energy, COALESCE(SUM(inference_time_ms), 0) as time_ms FROM ${TABLE}`,\n );\n const row = result.rows[0];\n return {\n totalMessages: Number(row.cnt),\n totalEmissionsUg: Number(row.emissions),\n totalEnergyWms: Number(row.energy),\n totalTimeMs: Number(row.time_ms),\n };\n}\n\n// ── Energy Question Submissions ────────────────────────────────────────\n\nexport interface SubmissionRecord {\n email: string;\n week: number;\n rawAnswer: string;\n}\n\nexport async function findSubmission(\n email: string,\n week: number,\n): Promise<{ email: string; week: number; rawAnswer: string } | null> {\n const result = await sqlite.execute({\n sql: `SELECT email, week, raw_answer FROM ${EQ_SUBMISSIONS_TABLE} WHERE email = ? AND week = ?`,\n args: [email, week],\n });\n if (result.rows.length === 0) return null;\n const row = result.rows[0];\n return {\n email: String(row.email),\n week: Number(row.week),\n rawAnswer: String(row.raw_answer),\n };\n}\n\nexport async function upsertSubmission(\n record: SubmissionRecord,\n): Promise<{ isUpdate: boolean }> {\n const existing = await findSubmission(record.email, record.week);\n await sqlite.execute({\n sql: `INSERT INTO ${EQ_SUBMISSIONS_TABLE} (email, week, raw_answer)\n VALUES (?, ?, ?)\n ON CONFLICT(email, week) DO UPDATE SET\n raw_answer = excluded.raw_answer,\n updated_at = datetime('now')`,\n args: [record.email, record.week, record.rawAnswer],\n });\n return { isUpdate: existing !== null };\n}\n",[],[218,456,448],"database","7223cf3a-1701-11f1-a39c-42dde27851f2","migrations.ts","2f1f586c-0e69-11f1-9845-42dde27851f2",141,["D",1772543040242],"import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\n\nconst IMPACT_TABLE = \"greenpt_impact_v1\";\nconst EQ_SUBMISSIONS_TABLE = \"greenpt_eq_submissions_v1\";\n\nexport async function runMigrations() {\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS ${IMPACT_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n session_id TEXT,\n emissions_ug REAL NOT NULL DEFAULT 0,\n energy_wms REAL NOT NULL DEFAULT 0,\n inference_time_ms REAL NOT NULL DEFAULT 0,\n created_at TEXT NOT NULL DEFAULT (datetime('now'))\n )`);\n\n await sqlite.execute(`CREATE TABLE IF NOT EXISTS ${EQ_SUBMISSIONS_TABLE} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n email TEXT NOT NULL,\n week INTEGER NOT NULL,\n raw_answer TEXT NOT NULL,\n created_at TEXT NOT NULL DEFAULT (datetime('now')),\n updated_at TEXT NOT NULL DEFAULT (datetime('now')),\n UNIQUE(email, week)\n )`);\n}\n",[],[218,456,458],"2ef967e2-0e69-11f1-a786-42dde27851f2",96,["D",1771598034757],[259,260],[218,456],["D",1771411796427],[258,261,262,263,264,265],[218],"8ea42c6a-0d7c-11f1-85b2-42dde27851f2",".gitkeep","8ea42d3c-0d7c-11f1-85b2-42dde27851f2",28,["D",1771496404535],"\n",[],[224,474],["D",1771496404338],[256],[224],["D",1771597882207],[],[230],"17803f5c-1655-11f1-8694-42dde27851f2","settings.local.json","0e28cc12-0e53-11f1-8dba-42dde27851f2",136,["D",1772469014741],"{\n \"permissions\": {\n \"allow\": [\n \"WebSearch\",\n \"WebFetch(domain:greenpt.com)\",\n \"mcp__ide__getDiagnostics\",\n \"WebFetch(domain:docs.langchain.com)\",\n \"WebFetch(domain:langfuse.com)\",\n \"WebFetch(domain:app.electricitymaps.com)\"\n ]\n }\n}\n",[],[236,488],["D",1771588530588],[253],[236],["D",1771411410564],{"_94":93,"_102":98},{"_94":93,"_95":96,"_97":98,"_18":-5,"_99":100,"_101":24},{"_13":83,"_31":84,"_41":40,"_85":42,"_86":-5,"_87":-5,"_88":-5,"_89":-5,"_90":-5,"_91":43,"_92":93},"ctx",{"_76":-5,"_122":535,"_536":537,"_538":539,"_540":24,"_541":24,"_542":24,"_543":120,"_544":-5,"_545":-7,"_546":-7,"_547":60,"_548":24,"_549":24,"_550":-5,"_551":24,"_552":24,"_553":24,"_554":-5},"ENV",{"_506":507,"_508":509,"_510":84,"_511":512,"_513":514,"_515":516,"_517":518,"_519":520,"_521":522,"_523":524,"_525":526,"_527":528,"_529":530,"_531":532,"_533":534},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","e87eb04e707cf64e7e532c8e8ec395f209e17f15","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

Diff · v162