بوابة OpenAI — محرك Val Town الأصلي

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🎮 منصّة التجربة المباشرة

\n التحفيز: 0\n الإخراج: 0\n الإجمالي: 0\n —\n

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🤖

مرحباً! أنا مُشغّل عبر البوابة المتوافقة مع OpenAI، والمحرّك الداخلي هو Val Town. جرّب سؤالاً من الأسئلة المقترحة 👇

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\n مقترحات:\n \n \n \n \n

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⚙️ إعدادات متقدمة

\n النظام (System Prompt)\n \n \n درجة الحرارة (Temperature)\n \n \n الحد الأقصى (Max Tokens)\n \n \n النموذج\n \n \n الجهد (Effort — للموديلات التفكيرية)\n \n \n

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🔌 بيانات الاتصال

\n المفتاح\n …\n \n \n

\n Base URL\n …\n \n

\n النموذج\n …\n \n

إعداد Cline / Roo Code / Cursor

انسخ المفتاح من الأعلى.
عيّن baseUrl = {BASE_URL} في مزوّد OpenAI مخصّص.
الصق المفتاح في حقل API Key.
احفظ كالتالي: v1/chat/completions.

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📦 مقتطفات التكامل

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# Cline — إعداد OpenAI-متوافق\nopenai-compatible:\n  baseUrl: {BASE_URL}\n  apiKey: {KEY}\n  modelId: {MODEL}

# في إعدادات Cline → OpenAI-متوافق\nOPENAI_BASE_URL={BASE_URL}\nOPENAI_API_KEY={KEY}\nOPENAI_MODEL_ID={MODEL}

curl {BASE_URL}/chat/completions \\\n  -H \"Authorization: Bearer {KEY}\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"{MODEL}\",\n    \"stream\": true,\n    \"messages\": [{\"role\":\"user\",\"content\":\"مرحباً\"}]\n  }'

from openai import OpenAI\n\nclient = OpenAI(\n    api_key=\"{KEY}\",\n    base_url=\"{BASE_URL}\",\n)\nresp = client.chat.completions.create(\n    model=\"{MODEL}\",\n    stream=True,\n    messages=[{\"role\":\"user\",\"content\":\"مرحباً\"}],\n)\nfor chunk in resp:\n    print(chunk.choices[0].delta.content or \"\", end=\"\")

import OpenAI from \"openai\";\n\nconst client = new OpenAI({\n  apiKey: \"{KEY}\",\n  baseURL: \"{BASE_URL}\",\n});\nconst stream = await client.chat.completions.create({\n  model: \"{MODEL}\",\n  stream: true,\n  messages: [{ role: \"user\", content: \"مرحباً\" }],\n});\nfor await (const chunk of stream) {\n  process.stdout.write(chunk.choices[0]?.delta?.content ?? \"\");\n}

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📊 النشاط والاستخدام

لا يوجد نشاط بعد.

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\n\n \n \n\n\n",[19,60],"2c10089c-e000-44b1-9f5e-0e106eef04b1","app.ts","0d200346-a6ea-11f1-bb4a-1607ee4eb77e",17,["D",1788366386984],4,"script","/**\n * public/app.ts — client-side UI for the OpenAI-compatible gateway.\n * Served (transpiled) by main.ts via serveFile.\n */\nimport { marked } from \"https://esm.sh/marked@14.1.3\";\nimport hljs from \"https://esm.sh/highlight.js@11.10.0\";\n\nmarked.setOptions({ gfm: true, breaks: true });\n\ntype Msg = { role: string; content: string };\n\nconst state = {\n apiKey: \"\",\n baseUrl: \"\",\n model: \"gpt-5.4-nano\",\n messages: [] as Msg[],\n controller: null as AbortController | null,\n busy: false,\n t0: 0,\n usage: { prompt: 0, completion: 0 } as { prompt: number; completion: number },\n};\n\nconst $ = (s: string): HTMLElement => document.querySelector(s) as HTMLElement;\nconst $$ = (s: string): NodeListOf => document.querySelectorAll(s);\n\nfunction est(s: string): number {\n if (!s) return 0;\n return Math.max(1, Math.ceil(s.length / 3));\n}\nfunction scrollBottom(el: HTMLElement) {\n el.scrollTop = el.scrollHeight;\n}\nasync function copy(text: string) {\n try {\n await navigator.clipboard.writeText(text);\n } catch {\n const ta = document.createElement(\"textarea\");\n ta.value = text;\n document.body.appendChild(ta);\n ta.select();\n document.execCommand(\"copy\");\n ta.remove();\n }\n}\n\n// ---- Boot -----------------------------------------------------------------\nasync function init() {\n await loadConfig();\n fillSnippets();\n wire();\n renderMessages();\n updateMetrics();\n setInterval(checkHealth, 8000);\n}\n\nasync function loadConfig() {\n state.baseUrl = location.origin + \"/api/provider/v1\";\n try {\n const r = await fetch(\"/api/provider/config\");\n const c = await r.json();\n state.apiKey = c.apiKey || \"\";\n state.model = c.model || \"gpt-5.4-nano\";\n $(\"#key-display\").textContent = (c.maskedKey || state.apiKey);\n $(\"#key-display\").dataset.full = state.apiKey;\n $(\"#key-display\").dataset.masked = c.maskedKey || state.apiKey;\n $(\"#base-url\").textContent = state.baseUrl;\n $(\"#model-display\").textContent = state.model;\n const sel = $(\"#model\") as HTMLSelectElement;\n sel.value = state.model;\n if (!sel.value) {\n const opt = document.createElement(\"option\");\n opt.value = state.model;\n opt.textContent = state.model;\n opt.selected = true;\n sel.insertBefore(opt, sel.firstChild);\n }\n const eff = $(\"#effort\") as HTMLSelectElement;\n if (eff && !eff.value) eff.value = \"medium\";\n } catch (e) {\n $(\"#key-display\").textContent = \"غير متاح\";\n $(\"#base-url\").textContent = state.baseUrl;\n }\n}\n\nfunction fillSnippets() {\n const rep: Record = {\n \"{BASE_URL}\": state.baseUrl,\n \"{KEY}\": state.apiKey,\n \"{MODEL}\": state.model,\n };\n $$(\".pane code\").forEach((el) => {\n let t = el.textContent || \"\";\n for (const k in rep) t = t.split(k).join(rep[k]);\n el.textContent = t;\n });\n}\n\nasync function checkHealth() {\n const badge = $(\"#status-badge\");\n const label = $(\"#status-label\");\n try {\n const r = await fetch(state.baseUrl + \"/models\");\n if (r.ok) {\n badge.className = \"status-badge status-ok\";\n label.textContent = \"المزوّد متصل\";\n } else throw new Error(\"bad\");\n } catch {\n badge.className = \"status-badge status-bad\";\n label.textContent = \"غير متصل\";\n }\n}\n\n// ---- Rendering -------------------------------------------------------------\nfunction renderMessages() {\n const box = $(\"#messages\");\n box.innerHTML = \"\";\n box.appendChild(welcomeMsg());\n for (const m of state.messages) {\n box.appendChild(msgNode(m.role, m.content, false));\n }\n scrollBottom(box);\n}\n\nfunction welcomeMsg(): HTMLElement {\n const div = document.createElement(\"div\");\n div.className = \"msg msg-assistant\";\n div.innerHTML =\n `

🤖

مرحباً! أنا مشغّل عبر البوابة المتوافقة مع OpenAI، والمحرّك الداخلي هو Val Town. جرّب سؤالاً من الأسئلة المقترحة 👇

`;\n return div;\n}\n\nfunction msgNode(role: string, content: string, caret: boolean): HTMLElement {\n const wrap = document.createElement(\"div\");\n wrap.className = \"msg \" + (role === \"user\" ? \"msg-user\" : \"msg-assistant\");\n const avatar = document.createElement(\"div\");\n avatar.className = \"avatar\";\n avatar.textContent = role === \"user\" ? \"🧑\" : \"🤖\";\n const bubble = document.createElement(\"div\");\n bubble.className = \"bubble\";\n wrap.append(avatar, bubble);\n paint(bubble, content, caret);\n return wrap;\n}\n\nfunction paint(bubble: HTMLElement, md: string, caret: boolean) {\n const html = marked.parse(md || \"…\") as string;\n bubble.innerHTML = html + (caret ? '▍' : \"\");\n enhance(bubble);\n}\n\nfunction enhance(container: HTMLElement) {\n container.querySelectorAll(\"pre > code\").forEach((code) => {\n try {\n hljs.highlightElement(code as HTMLElement, { ignoreUnescaped: true });\n } catch { /* keep plain */ }\n const pre = code.parentElement as HTMLElement;\n const btn = document.createElement(\"button\");\n btn.className = \"copy-btn\";\n btn.textContent = \"نسخ\";\n btn.addEventListener(\"click\", async () => {\n await copy((code as HTMLElement).innerText);\n btn.textContent = \"✓\";\n setTimeout(() => (btn.textContent = \"نسخ\"), 1200);\n });\n pre.appendChild(btn);\n });\n}\n\n// ---- Send / stream ----------------------------------------------------------\nfunction addAssistantBubble(): HTMLElement {\n const box = $(\"#messages\");\n const n = msgNode(\"assistant\", \"\", false);\n box.appendChild(n);\n scrollBottom(box);\n return n.querySelector(\".bubble\") as HTMLElement;\n}\n\nasync function send(prompt?: string) {\n if (state.busy) return;\n const input = $(\"#input\") as HTMLTextAreaElement;\n const text = (prompt ?? input.value.trim()).trim();\n if (!text) return;\n input.value = \"\";\n state.messages.push({ role: \"user\", content: text });\n state.usage = { prompt: 0, completion: 0 };\n renderMessages();\n\n const sys = ($(\"#sys-prompt\") as HTMLTextAreaElement).value.trim();\n const temp = parseFloat(($(\"#temperature\") as HTMLInputElement).value);\n const maxTok = parseInt(($(\"#max-tokens\") as HTMLInputElement).value, 10) || 2048;\n const model = ($(\"#model\") as HTMLSelectElement).value || state.model;\n const effort = ($(\"#effort\") as HTMLSelectElement).value;\n\n const messages: Msg[] = [\n ...(sys ? [{ role: \"system\", content: sys }] : []),\n ...state.messages,\n ];\n const body: Record = {\n model,\n messages,\n temperature: Number.isFinite(temp) ? temp : 0.7,\n max_tokens: maxTok,\n stream: true,\n };\n // Pass reasoning effort for thinking models when the user chooses one.\n if (effort) {\n body.reasoning_effort = effort;\n body.effort = effort;\n }\n\n state.controller = new AbortController();\n state.busy = true;\n state.t0 = performance.now();\n const stopBtn = $(\"#stop-btn\");\n ( $(\"#send-btn\") as HTMLButtonElement ).style.display = \"none\";\n stopBtn.classList.remove(\"hidden\");\n\n const bubble = addAssistantBubble();\n let out = \"\";\n let failed = false;\n\n try {\n const res = await fetch(state.baseUrl + \"/chat/completions\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\", Authorization: `Bearer ${state.apiKey}` },\n body: JSON.stringify(body),\n signal: state.controller.signal,\n });\n if (!res.ok || !res.body) {\n const errText = await res.text().catch(() => \"\");\n failed = true;\n out = `**خطأ (${res.status})**\\n\\n\\`\\`\\`\\n${(errText || res.statusText || \"فشل الطلب\").slice(0, 2000)}\\n\\`\\`\\``;\n paint(bubble, out, false);\n } else {\n const reader = res.body.getReader();\n const dec = new TextDecoder();\n let buf = \"\";\n for (;;) {\n const { done, value } = await reader.read();\n if (done) break;\n buf += dec.decode(value, { stream: true });\n let idx: number;\n while ((idx = buf.indexOf(\"\\n\")) >= 0) {\n const line = buf.slice(0, idx).trim();\n buf = buf.slice(idx + 1);\n if (!line.startsWith(\"data:\")) continue;\n const data = line.slice(5).trim();\n if (data === \"[DONE]\") continue;\n try {\n const json = JSON.parse(data);\n if (json.error) {\n failed = true;\n out = `**خطأ من المحرك:** ${json.error.message || \"\"}`;\n paint(bubble, out, false);\n continue;\n }\n const delta = json.choices?.[0]?.delta?.content;\n if (typeof delta === \"string\") {\n out += delta;\n paint(bubble, out, true);\n }\n if (json.usage) state.usage = json.usage;\n } catch { /* partial/keepalive */ }\n }\n }\n // final paint (no caret)\n const finalNode = bubble;\n const html = marked.parse(out || \"(لا مخرجات)\") as string;\n finalNode.innerHTML = html;\n enhance(finalNode);\n }\n } catch (e) {\n const err = e as Error;\n if (err.name !== \"AbortError\") {\n failed = true;\n out = (out || \"\") + `\\n\\n**خطأ:** ${err.message || \"تعذّر الاتصال\"}`;\n }\n paint(bubble, out || \"(أوقفت الإرسال)\", false);\n } finally {\n state.messages.push({ role: \"assistant\", content: out });\n state.busy = false;\n state.controller = null;\n ( $(\"#send-btn\") as HTMLButtonElement ).style.display = \"\";\n stopBtn.classList.add(\"hidden\");\n state.usage.completion = state.usage.completion || est(out);\n updateMetrics();\n addLog(model, failed, maxTok, state.messages.length);\n renderMessages();\n }\n}\n\nfunction stop() {\n state.controller?.abort();\n}\n\n// ---- Activity + metrics ------------------------------------------------------\nfunction updateMetrics() {\n let prompt = 0;\n let completion = 0;\n for (const m of state.messages) {\n if (m.role === \"assistant\") completion += est(m.content);\n else prompt += est(m.content);\n }\n $(\"#m-prompt\").textContent = \"التحفيز: \" + prompt;\n $(\"#m-completion\").textContent = \"الإخراج: \" + completion;\n $(\"#m-total\").textContent = \"الإجمالي: \" + (prompt + completion);\n const ms = Math.round(performance.now() - state.t0);\n $(\"#m-time\").textContent = ms >= 0 && state.busy === false && state.messages.length > 0\n ? `${ms.toLocaleString(\"ar\")} ms`\n : \"—\";\n}\n\nfunction addLog(model: string, failed: boolean, maxTok: number, n: number) {\n const box = $(\"#activity\");\n const empty = box.querySelector(\".empty\");\n if (empty) empty.remove();\n const item = document.createElement(\"div\");\n item.className = \"log-item\";\n const now = new Date();\n item.innerHTML =\n `

${failed ? \"✖ فشل\" : \"✓ نجاح\"}${model}

` +\n `

SSE stream • max ${maxTok} tok • ${n} رسالة • ${now.toLocaleTimeString(\"ar\")}

`;\n box.prepend(item);\n while (box.children.length > 8) box.lastElementChild?.remove();\n}\n\n// ---- Export & save ------------------------------------------------------------\nfunction buildExport(fmt: string): string {\n const md = state.messages.map((m) => `**${m.role}:**\\n${m.content}`).join(\"\\n\\n---\\n\\n\");\n if (fmt === \"md\") return `# محادثة بوابة OpenAI\\n\\n${md}`;\n if (fmt === \"csv\") {\n const lines = [[\"role\", \"content\"].join(\"\")];\n return state.messages.map((m) => `${m.role},\"${m.content.replace(/\"/g, '\"\"').replace(/\\n/g, \" \")}\"`).join(\"\\n\");\n }\n if (fmt === \"curl\") {\n const msgs = JSON.stringify(state.messages.map((m) => ({ role: m.role, content: m.content })));\n return `curl ${state.baseUrl}/chat/completions -H \"Authorization: Bearer ${state.apiKey}\" -H \"Content-Type: application/json\" -d '{\"model\":\"${state.model}\",\"stream\":true,\"messages\":${msgs}}'`;\n }\n return md;\n}\n\nfunction download(name: string, content: string, type: string) {\n const a = document.createElement(\"a\");\n a.href = URL.createObjectURL(new Blob([content], { type }));\n a.download = name;\n a.click();\n URL.revokeObjectURL(a.href);\n}\n\nfunction saveChat() {\n if (!state.messages.length) return;\n const key = \"saved-chats-\" + location.pathname;\n let list: string[] = [];\n try { list = JSON.parse(localStorage.getItem(key) || \"[]\"); } catch { list = []; }\n const title = (state.messages[0]?.content || \"محادثة\").slice(0, 30);\n list.unshift(JSON.stringify({ title, messages: state.messages }));\n list = list.slice(0, 8);\n localStorage.setItem(key, JSON.stringify(list));\n renderSaved();\n}\n\nfunction renderSaved() {\n const bar = $(\"#saved-bar\");\n bar.innerHTML = \"\";\n const key = \"saved-chats-\" + location.pathname;\n let list: string[] = [];\n try { list = JSON.parse(localStorage.getItem(key) || \"[]\"); } catch { list = []; }\n list.forEach((raw, i) => {\n let t = `محادثة ${i + 1}`;\n try { t = JSON.parse(raw).title as string; } catch { /* ignore */ }\n const b = document.createElement(\"button\");\n b.className = \"chip saved-chip\";\n b.textContent = \"💾 \" + t + \" ⤴\";\n b.addEventListener(\"click\", () => {\n try {\n state.messages = JSON.parse(raw).messages as Msg[];\n renderMessages();\n updateMetrics();\n } catch { /* ignore */ }\n });\n bar.appendChild(b);\n });\n}\n\n// ---- Wiring ----------------------------------------------------------------\nfunction wire() {\n $(\"#theme-toggle\").addEventListener(\"click\", () => {\n const cur = document.documentElement.getAttribute(\"data-theme\");\n const next = cur === \"dark\" ? \"light\" : \"dark\";\n document.documentElement.setAttribute(\"data-theme\", next);\n ($(\"#theme-toggle\") as HTMLButtonElement).textContent = next === \"dark\" ? \"☀️\" : \"🌙\";\n try { localStorage.setItem(\"theme\", next); } catch { /* ignore */ }\n });\n const savedTheme = (() => { try { return localStorage.getItem(\"theme\"); } catch { return null; } })();\n if (savedTheme) {\n document.documentElement.setAttribute(\"data-theme\", savedTheme);\n ($(\"#theme-toggle\") as HTMLButtonElement).textContent = savedTheme === \"dark\" ? \"☀️\" : \"🌙\";\n }\n\n $(\"#send-btn\").addEventListener(\"click\", () => send());\n $(\"#stop-btn\").addEventListener(\"click\", stop);\n const input = $(\"#input\") as HTMLTextAreaElement;\n input.addEventListener(\"keydown\", (e) => {\n if (e.key === \"Enter\" && !e.shiftKey) {\n e.preventDefault();\n send();\n }\n });\n\n $$(\".preset\").forEach((btn) =>\n btn.addEventListener(\"click\", () => send((btn as HTMLButtonElement).dataset.p)));\n\n $(\"#reveal-key\").addEventListener(\"click\", () => {\n const el = $(\"#key-display\");\n const full = el.dataset.full || \"\";\n el.textContent = el.textContent === full ? (el.dataset.masked || full) : full;\n });\n $(\"#copy-key\").addEventListener(\"click\", () => copy(state.apiKey));\n $(\"#copy-url\").addEventListener(\"click\", () => copy(state.baseUrl));\n $(\"#copy-model\").addEventListener(\"click\", () => copy(state.model));\n\n $(\"#save-chat\").addEventListener(\"click\", saveChat);\n renderSaved();\n\n $(\"#export-btn\").addEventListener(\"click\", (e) => {\n e.stopPropagation();\n const m = $(\"#export-menu\");\n m.classList.toggle(\"hidden\");\n });\n document.addEventListener(\"click\", () => $(\"#export-menu\").classList.add(\"hidden\"));\n $$(\"#export-menu button\").forEach((b) =>\n b.addEventListener(\"click\", () => {\n const fmt = (b as HTMLButtonElement).dataset.fmt || \"md\";\n const content = buildExport(fmt);\n download(`chat.${fmt === \"md\" ? \"md\" : fmt === \"csv\" ? \"csv\" : \"sh\"}`, content, \"text/plain\");\n $(\"#export-menu\").classList.add(\"hidden\");\n }));\n\n $$(\".tab\").forEach((t) =>\n t.addEventListener(\"click\", () => {\n $$(\".tab\").forEach((x) => x.classList.remove(\"active\"));\n t.classList.add(\"active\");\n const id = (t as HTMLButtonElement).dataset.t || \"\";\n $$(\".pane\").forEach((p) => p.classList.toggle(\"hidden\", p.id !== id));\n }));\n\n $$(\".conn-row\").forEach(() => { /* noop */ });\n}\n\ninit();\n",[19,68],"16e6fe0e-8f62-479a-a6d3-e2c6ec070892","directory",0,["D",1788365874885],5,[19],{"_36":137,"_13":138,"_39":139,"_41":-5,"_43":44,"_45":51,"_47":48,"_49":140,"_17":78,"_52":77,"_53":-5,"_55":56,"_57":-5,"_95":141,"_18":142},{"_36":76,"_13":19,"_39":42,"_41":-5,"_43":44,"_45":80,"_47":48,"_49":122,"_17":78,"_52":77,"_53":-5,"_55":56,"_57":-5,"_95":123,"_18":124},{"_36":115,"_13":116,"_39":117,"_41":-5,"_43":44,"_45":118,"_47":48,"_49":119,"_17":78,"_52":77,"_53":-5,"_55":56,"_57":-5,"_95":120,"_18":121},{"_36":108,"_13":109,"_39":110,"_41":-5,"_43":44,"_45":64,"_47":48,"_49":111,"_17":78,"_52":24,"_53":112,"_55":56,"_57":-5,"_95":113,"_18":114},{"_36":98,"_13":99,"_39":100,"_41":-5,"_43":44,"_45":101,"_47":48,"_49":102,"_17":51,"_52":103,"_53":104,"_55":56,"_57":105,"_95":106,"_18":107},{"_36":88,"_13":89,"_39":90,"_41":-5,"_43":44,"_45":91,"_47":48,"_49":92,"_17":93,"_52":24,"_53":94,"_55":56,"_57":-5,"_95":96,"_18":97},"13f84f84-394d-4a64-b360-efcfdfe54539","worklog.md","c9f16d1c-a6e9-11f1-8b7e-1607ee4eb77e",13,["D",1788366028592],1,"# Worklog — OpenAI-Compatible Gateway\n\nNative engine: **Val Town `std/openai`** (ValTownOpenAI proxy, ambient `valtown` token, no external key).\n\n| # | Stage | Status |\n|---|-------|--------|\n| | Environment discovery (native AI, no keys) | ✅ `std/openai` found (`ValTownOpenAI`) |\n| 1 | Generated gateway key, stored `PROVIDER_API_KEY` | ✅ `sk-q7X2mN9vTzQ4rL8pKc3eF6sUa1dB5hYw` |\n| 2 | `lib/bridge.ts` — bridge, sanitizer, clamp, auto-heal, SSE | ✅ |\n| 3 | `main.ts` http router (`/api/provider/v1/*`, config, /health, assets) | ✅ |\n| 4 | Frontend RTL `/` (playground, info, snippets, usage) | ✅ |\n| 5 | curl / fetch — 401 on missing key | ✅ verified |\n| 6 | curl / fetch — 200 + SSE streaming (`data:` → `[DONE]`) | ✅ verified |\n| 7 | Non-streaming 200 + usage | ✅ verified |\n| 8 | Endpoints: `/health`, `/models`, `/config`, `/`, `/app.ts` | ✅ verified |\n| 9 | Subdomain `openai-gateway-city.val.run` | ✅ |\n\n## Verification evidence\n- `GET /health` → `{\"status\":\"ok\",\"service\":\"internal-bridge\"}`\n- `GET /api/provider/v1/models` → OpenAI `object:\"list\"`, `id:\"gpt-5.4-nano\"`\n- `POST /v1/chat/completions` without key → **401** (OpenAI error shape)\n- `POST` with key + `stream:true` → **200** `Content-Type: text/event-stream`, token-by-token `data: {...}` chunks ending `data: [DONE]`\n- `POST` with key + `stream:false` → **200** `{\"content\":\"نعم\",\"usage\":{...}}`\n- `/` serves `lang=\"ar\" dir=\"rtl\"` HTML; `/app.ts` transpiles to `text/javascript`\n","children",[],[89],"af6f56dd-9f96-4384-85bf-726dc62502d3","main.ts","aeb5b076-a6e9-11f1-8b7e-1607ee4eb77e",8,["D",1788365914409],"http","/**\n * main.ts (http) — Router entrypoint for the OpenAI-compatible gateway.\n *\n * Mounts the internal-provider bridge under /api/provider/v1, serves the\n * RTL frontend, static assets, and the service config endpoint.\n */\nimport { serveFile } from \"https://esm.town/v/std/utils/index.ts\";\nimport { handleApi, config } from \"./lib/bridge.ts\";\n\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const p = url.pathname;\n\n // Public config consumed by the UI (model + generated key).\n if (p === \"/api/provider/config\") {\n return Response.json(await config());\n }\n\n // Service health (open, no auth).\n if (p === \"/health\") {\n return Response.json({ status: \"ok\", service: \"internal-bridge\" });\n }\n\n // The OpenAI-compatible gateway (mounted at /api/provider/v1).\n if (p === \"/api/provider/v1\" || p.startsWith(\"/api/provider/v1/\")) {\n const sub = p === \"/api/provider/v1\" ? \"/\" : p.slice(\"/api/provider/v1\".length);\n return handleApi(req, sub);\n }\n\n // Frontend + assets.\n if (p === \"/\") return serveFile(\"/public/index.html\");\n if (p === \"/styles.css\") return serveFile(\"/public/styles.css\");\n if (p === \"/app.ts\") return serveFile(\"/public/app.ts\");\n\n return new Response(\"Not found\", { status: 404 });\n}\n","openai-gateway-city",[],[99],"176b522f-1a0f-4166-988f-f7371feec9a5","README.md","e7f75c0e-a6e9-11f1-9ab5-1607ee4eb77e",["D",1788365897439],"# OpenAI-Compatible Gateway on Val Town\n\nA production-ready **OpenAI-compatible API gateway** whose upstream engine is\n**Val Town's native AI** (`std/openai` → the ValTownOpenAI proxy, authenticated\nby the ambient `valtown` token). **No external API key required** — Cline, Roo\nCode, Cursor, the Python OpenAI SDK, or cURL can all connect to this gateway\nand chat through real SSE streaming.\n\n## Quick start\n\n- **Base URL:** `{origin}/api/provider/v1`\n- **Model:** `gpt-5.4-nano` (Val Town native)\n- **API Key:** shown in the UI (stored server-side in `PROVIDER_API_KEY`)\n\n### Cline / Roo Code / Cursor (OpenAI-compatible provider)\n\n```\nbaseUrl = {origin}/api/provider/v1\napiKey = \nmodelId = gpt-5.4-nano\n```\n\n### cURL\n\n```bash\ncurl {origin}/api/provider/v1/chat/completions \\\n -H \"Authorization: Bearer \" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"gpt-5.4-nano\",\"stream\":true,\n \"messages\":[{\"role\":\"user\",\"content\":\"مرحباً\"}]}'\n```\n\n## Endpoints\n\n| Method | Path | Auth | Description |\n|--------|------|------|-------------|\n| GET | `/health` | no | `{status:\"ok\", service:\"internal-bridge\"}` |\n| GET | `/api/provider/v1` | no | Service info & limits |\n| GET | `/api/provider/v1/models` | no | Active model, OpenAI format |\n| POST | `/api/provider/v1/chat/completions` | **yes** | Chat (SSE or non-streaming) |\n| GET | `/api/provider/config` | no | `{model, apiKey, keyRequired}` for the UI |\n\n## Architecture\n\n- `main.ts` (http) — router: mounts the gateway under `/api/provider/v1`,\n serves the RTL frontend and assets.\n- `lib/bridge.ts` (script) — the internal provider bridge: `sanitizeMessages`\n (never throws), `max_tokens` clamping, auto-healing singleton that re-reads\n platform credentials, and real SSE streaming via the OpenAI SDK.\n- `public/` — Arabic RTL UI (playground, connection info, integration\n snippets, activity/usage).\n\n## Environment variables\n\n- `PROVIDER_API_KEY` — the gateway bearer key (required on chat completions).\n- `PROVIDER_MODEL` — optional, overrides the default `gpt-5.4-nano`.\n\n## Notes & limits\n\n- Native engine rate limit: ~10 requests/minute (Val Town proxy).\n- Pro users get 10 expensive-model requests per rolling 24h, then bump to nano.\n- `max_tokens` is auto-clamped to the engine ceiling to avoid upstream errors.\n",[],[109],"6f16390d-fc45-4fe5-b99c-552fa840465c","scripts","0bbfaac7-0d86-4181-bfce-759b958dfb7a",24,["D",1788367750585],[],[116],["D",1788365874885],[125,126,127],[19],{"_36":67,"_13":68,"_39":69,"_41":42,"_43":44,"_45":70,"_47":48,"_49":134,"_17":72,"_52":73,"_53":74,"_55":56,"_57":-5,"_95":135,"_18":136},{"_36":59,"_13":60,"_39":61,"_41":42,"_43":44,"_45":62,"_47":48,"_49":131,"_17":64,"_52":24,"_53":65,"_55":56,"_57":-5,"_95":132,"_18":133},{"_36":37,"_13":38,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":128,"_17":51,"_52":24,"_53":54,"_55":56,"_57":-5,"_95":129,"_18":130},["D",1788365914540],[],[19,38],["D",1788368708108],[],[19,60],["D",1788366386984],[],[19,68],"7b86a1e8-2571-4317-82ca-1d10a6ce8654","lib","e2a0cbeb-2016-432b-9f07-0defe6ee8dbf",["D",1788365847017],[143],[138],{"_36":144,"_13":145,"_39":146,"_41":139,"_43":44,"_45":147,"_47":48,"_49":148,"_17":80,"_52":73,"_53":149,"_55":56,"_57":-5,"_95":150,"_18":151},"2790bd94-ec1e-4614-a085-ce240493edf3","bridge.ts","c9e9cd50-a6e9-11f1-8b7e-1607ee4eb77e",45,["D",1788369399905],"/**\n * lib/bridge.ts — Internal Provider Bridge & Native AI Engine\n *\n * Maps standard OpenAI requests onto Val Town's native AI engine\n * (`std/openai` -> ValTownOpenAI proxy, authenticated via the ambient\n * `valtown` token — no external API key required).\n *\n * Exposes (mounted by main.ts under /api/provider/v1):\n * GET /models -> active native model, OpenAI format, no auth\n * POST /chat/completions -> chat (streaming real SSE or non-streaming)\n *\n * @service the \"port 3040 internal backend service\" of this deployment —\n * no extra process needed: it's a module singleton.\n */\nimport { OpenAI } from \"https://esm.town/v/std/openai/main.ts\";\n\n// ---- Configuration -------------------------------------------------------\n\n/** Native engine is Val Town's proxy; model is configurable. */\nexport const MODEL = (Deno.env.get(\"PROVIDER_MODEL\") || \"gpt-5.4-nano\").trim();\n/** Hard ceiling for output tokens to avoid upstream rejection errors. */\nconst MAX_OUTPUT_TOKENS = 16384;\n\n// ---- Singleton client with auto-healing ----------------------------------\n// If the ambient session token rotates or a transient error occurs, we\n// re-instantiate the client so it re-reads platform credentials.\n\nlet client: OpenAI | null = null;\n\n/** Returns (and lazily creates) the native engine client singleton. */\nexport function getClient(): OpenAI {\n if (!client) client = new OpenAI();\n return client;\n}\n\n/** Force a fresh client on the next call (auto-heal). */\nexport function resetClient(): void {\n client = null;\n}\n\n/**\n * Invoke the engine with an internal rate-limiter and auto-retry on 429.\n * - Throttles to ~9 requests/min so the proxy's ~10/min budget is never\n * exceeded from this gateway, keeping 429s rare.\n * - On a 429 (rate limit) it sleeps with backoff and retries.\n * - Auto-heal: resets the client singleton (re-reading credentials) on error.\n */\nconst MAX_REQ_PER_MIN = 9;\nconst engineTimes: number[] = [];\nfunction sleep(ms: number): Promise {\n return new Promise((r) => setTimeout(r, ms));\n}\nasync function throttleEngine(): Promise {\n const now = Date.now();\n while (engineTimes.length && now - engineTimes[0] >= 60000) engineTimes.shift();\n if (engineTimes.length >= MAX_REQ_PER_MIN) {\n // Wait until the oldest request ages out of the window.\n await sleep(60000 - (now - engineTimes[0]) + 100);\n engineTimes.shift();\n }\n engineTimes.push(Date.now());\n}\n\nasync function callEngine(payload: Record) {\n const attempts = 4;\n for (let i = 0; i < attempts; i++) {\n await throttleEngine();\n try {\n return await getClient().chat.completions.create(payload as never);\n } catch (e) {\n const err = e as { message?: string; status?: number };\n resetClient(); // auto-heal: re-read platform credentials\n // 429 = rate limited: back off and retry.\n if (err.status === 429 && i < attempts - 1) {\n await sleep(2600 * (i + 1));\n continue;\n }\n // One plain retry for transient errors, then surface.\n if (i === 0) continue;\n const status =\n err.status && err.status < 600 && err.status >= 400 ? err.status : 502;\n throw new UpstreamError(err.message || \"native engine failed\", status);\n }\n }\n throw new UpstreamError(\"engine retries exhausted (rate limit)\", 429);\n}\n\nclass UpstreamError extends Error {\n status: number;\n constructor(message: string, status = 502) {\n super(message);\n this.status = status;\n }\n}\n\n// ---- Authentication ------------------------------------------------------\n\n/** The generated gateway key (stored server-side in PROVIDER_API_KEY). */\nexport function apiKey(): string {\n return Deno.env.get(\"PROVIDER_API_KEY\") || \"\";\n}\n\n/** True if the request carries a valid bearer / x-api-key. */\nexport function isAuthed(req: Request): boolean {\n const header = req.headers.get(\"authorization\") || req.headers.get(\"x-api-key\") || \"\";\n const token = header.replace(/^Bearer\\s+/i, \"\").trim();\n return token.length > 0 && token === apiKey();\n}\n\nfunction unauthorized(): Response {\n return Response.json(\n {\n error: {\n message:\n \"Incorrect API key provided. Include `Authorization: Bearer ` or `X-Api-Key: `. Get your key from the gateway UI.\",\n type: \"invalid_request_error\",\n param: null,\n code: \"invalid_api_key\",\n },\n },\n { status: 401 },\n );\n}\n\n// ---- Message sanitization (never throws) ---------------------------------\n\nfunction toText(content: unknown): string {\n try {\n if (content == null) return \"\";\n if (typeof content === \"string\") return content;\n if (Array.isArray(content)) {\n return content\n .map((part) => {\n if (!part || typeof part !== \"object\") return \"\";\n const p = part as { type?: string; text?: string; image_url?: { url?: string } };\n if (p.type === \"text\") return String(p.text ?? \"\");\n if (p.type === \"image_url\") return \"\\[image\\]\";\n return \"\";\n })\n .filter(Boolean)\n .join(\"\\n\");\n }\n return String(content);\n } catch {\n return \"\";\n }\n}\n\n/**\n * Pass through plain system/user/assistant messages; safely flatten\n * tool / function roles and role-object shapes into clean text messages.\n * Guaranteed to never throw.\n */\nexport function sanitizeMessages(messages: unknown): { role: string; content: string }[] {\n try {\n if (!Array.isArray(messages)) return [{ role: \"user\", content: \"Hi\" }];\n const out: { role: string; content: string }[] = [];\n for (const raw of messages) {\n try {\n const msg = (raw ?? {}) as {\n role?: string | { role?: string };\n content?: unknown;\n tool_calls?: { function?: { name?: string } }[];\n name?: string;\n };\n let role = typeof msg.role === \"string\" ? msg.role : \"user\";\n let content = toText(msg.content);\n // Flatten assistant tool_calls into a note so nothing is lost.\n const calls = Array.isArray(msg.tool_calls)\n ? msg.tool_calls.filter(Boolean).map((t) => t?.function?.name || \"tool\")\n : [];\n if (calls.length && !content) {\n content = \"[invoked: \" + calls.join(\", \") + \"]\";\n }\n if (role === \"tool\" || role === \"function\") role = \"user\";\n // Merge adjacent same-role messages so alternating-role rules hold.\n const last = out[out.length - 1];\n if (last && last.role === role) {\n last.content += (last.content ? \"\\n\" : \"\") + content;\n continue;\n }\n out.push({ role, content });\n } catch {\n // drop malformed message\n }\n }\n // Leading assistant messages upset some engines — prepend a user turn.\n if (out.length && out[0].role === \"assistant\") {\n out.unshift({ role: \"user\", content: \"Continue.\" });\n }\n if (!out.length) out.push({ role: \"user\", content: \"Hi\" });\n return out;\n } catch {\n return [{ role: \"user\", content: \"Hi\" }];\n }\n}\n\n// ---- Payload shaping ------------------------------------------------------\n\nfunction clampOutputTokens(v: unknown, fallback = 1024): number {\n let n = Number(v ?? fallback);\n if (!Number.isFinite(n) || n <= 0) n = fallback;\n return Math.min(Math.floor(n), MAX_OUTPUT_TOKENS);\n}\n\nfunction buildPayload(body: Record, model: string) {\n const messages = sanitizeMessages(body.messages);\n const maxTokens = clampOutputTokens(\n body.max_tokens ?? body.max_completion_tokens,\n 2048,\n );\n // Reasoning/thinking models (GPT-5 family, o-series) reject temperature/top_p\n // (only the default (1) is supported) but accept reasoning_effort.\n const isReasoning = /^gpt-5|^o[0-9]|^o[0-9]-mini|reasons/.test(String(model));\n const supportsSampling = !isReasoning && !isClaude;\n // Claude models (via optional Anthropic bridge) also skip sampling params.\n const isClaude = /^claude|^anthropic/.test(String(model));\n\n const payload: Record = {\n model,\n messages,\n max_completion_tokens: maxTokens,\n };\n if (supportsSampling && typeof body.temperature === \"number\") payload.temperature = body.temperature;\n if (supportsSampling && typeof body.top_p === \"number\") payload.top_p = body.top_p;\n if (supportsSampling && typeof body.presence_penalty === \"number\") payload.presence_penalty = body.presence_penalty;\n if (supportsSampling && typeof body.frequency_penalty === \"number\") payload.frequency_penalty = body.frequency_penalty;\n if (!isClaude && typeof body.stop !== \"undefined\") payload.stop = body.stop;\n if (!isClaude && typeof body.user === \"string\") payload.user = body.user;\n if (!isClaude && typeof body.seed === \"number\") payload.seed = body.seed;\n // Reasoning effort is only meaningful (and accepted) on thinking models.\n if (isReasoning && !isClaude) {\n const effort =\n typeof body.reasoning_effort === \"string\" && body.reasoning_effort\n ? body.reasoning_effort\n : typeof body.effort === \"string\" && body.effort\n ? body.effort\n : \"\";\n if (effort) payload.reasoning_effort = effort;\n }\n // Reasoning models burn tokens \"thinking\" before the visible answer — make\n // sure the requested budget leaves room for an actual reply.\n if (isReasoning && maxTokens < 512) payload.max_completion_tokens = 512;\n return { payload, messages, maxTokens, isClaude };\n}\n\n// ---- Handlers -------------------------------------------------------------\n\nexport async function handleModels(): Promise {\n return Response.json({\n object: \"list\",\n data: [\n { id: MODEL, object: \"model\", created: Math.floor(Date.now() / 1000), owned_by: \"val-town\" },\n ],\n });\n}\n\nexport async function handleRootInfo(): Promise {\n const now = Date.now();\n return Response.json({\n service: \"val-town-openai-gateway\",\n status: \"ok\",\n engine: \"std/openai (Val Town native AI)\",\n model: MODEL,\n base_url: \"/api/provider/v1\",\n auth: \"Bearer PROVIDER_API_KEY required on /v1/chat/completions\",\n limits: {\n requests_per_minute: 10,\n max_output_tokens: MAX_OUTPUT_TOKENS,\n expiry: new Date(now).toISOString(),\n },\n endpoints: [\"/health\", \"/v1/models\", \"/v1/chat/completions\"],\n });\n}\n\n/** Public config consumed by the UI (exposes the generated key). */\nexport async function config(): Promise> {\n const key = apiKey();\n return {\n model: MODEL,\n apiKey: key,\n keyRequired: key.length > 0,\n maskedKey: key.length > 6 ? \"sk-\" + key.slice(3, 7) + \"••••\" + key.slice(-4) : key,\n };\n}\n\nexport async function handleChatCompletions(req: Request): Promise {\n if (!isAuthed(req)) return unauthorized();\n\n let body: Record;\n try {\n body = await req.json();\n } catch {\n return Response.json(\n { error: { message: \"Invalid or missing JSON body\", type: \"invalid_request_error\", param: null, code: \"invalid_request\" } },\n { status: 400 },\n );\n }\n if (!Array.isArray(body.messages)) {\n return Response.json(\n { error: { message: \"Field `messages` must be an array\", type: \"invalid_request_error\", param: \"messages\", code: \"invalid_request\" } },\n { status: 400 },\n );\n }\n\n const stream = body.stream === true || body.stream === \"true\";\n const model = typeof body.model === \"string\" && body.model ? String(body.model) : MODEL;\n const { payload, isClaude } = buildPayload(body, model);\n\n if (isClaude) return claudeChat(req, payload, stream);\n if (stream) return streamChat(payload);\n return nonStreamChat(payload);\n}\n\n/**\n * Optional Anthropic bridge: routes claude-* models to Anthropic's\n * OpenAI-compatible endpoint. Requires ANTHROPIC_API_KEY (none provided by\n * the platform — Val Town's native engine only covers OpenAI models).\n */\nasync function claudeChat(req: Request, payload: Record, stream: boolean): Promise {\n const key = Deno.env.get(\"ANTHROPIC_API_KEY\");\n if (!key) {\n return Response.json(\n {\n error: {\n message:\n \"Claude models need the ANTHROPIC_API_KEY environment variable to be set on this gateway before they can run. Add it in the val's env settings to enable Claude.\",\n type: \"invalid_request_error\",\n param: null,\n code: \"missing_anthropic_key\",\n },\n },\n { status: 400 },\n );\n }\n const target = \"https://api.anthropic.com/v1/openai/chat/completions\";\n const outBody: Record = { model: payload.model, messages: payload.messages };\n outBody.max_tokens = payload.max_completion_tokens ?? 2048;\n if (typeof payload.temperature === \"number\") outBody.temperature = payload.temperature;\n if (stream) outBody.stream = true;\n try {\n const upstream = await fetch(target, {\n method: \"POST\",\n headers: { Authorization: `Bearer ${key}`, \"Content-Type\": \"application/json\" },\n body: JSON.stringify(outBody),\n // Propagate client abort to the upstream Anthropic call.\n signal: req.signal,\n });\n const ct = upstream.headers.get(\"content-type\") || \"application/json\";\n return new Response(upstream.body, { status: upstream.status, headers: { \"Content-Type\": ct } });\n } catch (e) {\n const m = e instanceof Error ? e.message : String(e);\n return Response.json(\n { error: { message: \"Claude upstream error: \" + m, type: \"upstream_error\", param: null, code: \"upstream_error\" } },\n { status: 502 },\n );\n }\n}\n\nasync function nonStreamChat(payload: Record): Promise {\n let completion: any;\n try {\n completion = await callEngine(payload);\n } catch (e) {\n const u = e as UpstreamError;\n return Response.json(\n { error: { message: u.message, type: \"upstream_error\", param: null, code: \"upstream_error\" } },\n { status: u.status || 502 },\n );\n }\n const choice = completion?.choices?.[0] ?? {};\n return Response.json({\n id: completion?.id || `chatcmpl-${Date.now()}`,\n object: \"chat.completion\",\n created: completion?.created ?? Math.floor(Date.now() / 1000),\n model: completion?.model || payload.model,\n choices: [\n {\n index: 0,\n message: { role: \"assistant\", content: choice?.message?.content ?? \"\" },\n finish_reason: choice?.finish_reason ?? \"stop\",\n },\n ],\n usage: completion?.usage ?? null,\n });\n}\n\nasync function streamChat(payload: Record): Promise {\n // Start the upstream stream; reset + retry once on transient/token errors.\n // The OpenAI SDK returns an async-iterable stream of chunks for stream:true.\n let upstream: AsyncIterable;\n try {\n upstream = await callEngine({ ...payload, stream: true }) as AsyncIterable;\n } catch (e) {\n const u = e as UpstreamError;\n return Response.json(\n { error: { message: u.message, type: \"upstream_error\", param: null, code: \"upstream_error\" } },\n { status: u.status || 502 },\n );\n }\n\n const encoder = new TextEncoder();\n const sysTime = Math.floor(Date.now() / 1000);\n const model = String(payload.model);\n\n const raw = new ReadableStream({\n async start(controller) {\n try {\n for await (const chunk of upstream) {\n const line = { id: chunk?.id || `chatcmpl-${sysTime}`, object: \"chat.completion.chunk\", created: chunk?.created ?? sysTime, model: chunk?.model || model, choices: chunk?.choices ?? [] };\n controller.enqueue(encoder.encode(\"data: \" + JSON.stringify(line) + \"\\n\\n\"));\n }\n controller.enqueue(encoder.encode(\"data: [DONE]\\n\\n\"));\n } catch (e) {\n const msg = e instanceof Error ? e.message : String(e);\n controller.enqueue(encoder.encode(\"data: \" + JSON.stringify({ error: { message: msg, type: \"stream_error\" } }) + \"\\n\\n\"));\n } finally {\n try { controller.close(); } catch { /* already closed */ }\n }\n },\n cancel() {\n // Client aborted — propagate to upstream if it supports cancellation.\n try { upstream.return?.(undefined); } catch { /* ignore */ }\n },\n });\n\n return new Response(raw, {\n headers: {\n \"Content-Type\": \"text/event-stream\",\n \"Cache-Control\": \"no-cache, no-transform\",\n \"X-Accel-Buffering\": \"no\",\n Connection: \"keep-alive\",\n },\n });\n}\n\n/** Top-level dispatcher for paths mounted under /api/provider/v1. */\nexport async function handleApi(req: Request, sub: string): Promise {\n const method = req.method;\n 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