Multi-AI v29 PERFECT

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Answer in Hebrew, detailed, no limits.\nIf asked who built you / who developed the system / מי פיתח אותך - answer ONLY: \"המערכת פותחה על ידי מוטי כץ.\"\nOtherwise answer the user's question directly.\nNever mention other AI models, providers, or that you're part of a multi-model system.`;\n\nconst JSP =\n `You are the sole assistant. The user asked a question, and you received draft answers from internal sources. Synthesize them into ONE unified answer in Hebrew, 500-800 words, detailed and accurate.\n\nCRITICAL RULES - NEVER BREAK:\n1. NEVER mention sources, models, providers, or that there were multiple answers.\n2. NEVER use words: OpenRouter, Gemini, Groq, Dahl, Mistral, Cerebras, NVIDIA, Cohere, Z AI, models, providers, drafts, השוואתי, סינתזה.\n3. NEVER explain your process. Forbidden: \"אנתח\", \"אשווה בין\", \"המודלים ענו\", \"אסנתז\", \"נבצע ניתוח\", \"הסיכום של\".\n4. Just deliver ONE direct answer as if YOU are the only assistant in the world.\n5. If drafts contradict, silently pick the most accurate (prefer fresh web search).\n6. Format with headings/bullets/tables - as YOUR answer, not comparison.`;\n\n// ========== UTILS ==========\nconst rateLimitMap = new Map();\nfunction checkRateLimit(cid: string): boolean {\n const now = Date.now();\n const arr = rateLimitMap.get(cid) || [];\n const recent = arr.filter((t) => now - t < 60000);\n if (recent.length >= 30) return false;\n recent.push(now);\n rateLimitMap.set(cid, recent);\n return true;\n}\nasync function wt(p: Promise, ms: number, fb: T): Promise {\n let t: any;\n const tp = new Promise((r) => {\n t = setTimeout(() => r(fb), ms);\n });\n try {\n return await Promise.race([p, tp]);\n } finally {\n if (t) clearTimeout(t);\n }\n}\nasync function fr(\n url: string,\n o: RequestInit,\n mr = RETRIES,\n): Promise {\n let last: Response | null = null;\n for (let i = 0; i <= mr; i++) {\n const r = await fetch(url, o);\n if (r.status !== 429 && r.status !== 402) return r;\n last = r;\n if (i < mr) await new Promise((x) => setTimeout(x, RETRY_MS));\n }\n return last as Response;\n}\nfunction tr(s: string, n: number) {\n return !s ? \"\" : s.length <= n ? s : s.slice(0, n) + \"...\";\n}\nfunction et(s: string) {\n return s ? Math.max(1, Math.round(s.length / 4)) : 0;\n}\nfunction sj(d: unknown) {\n return JSON.stringify(d).replace(//g, \"\\\\u003e\")\n .replace(/&/g, \"\\\\u0026\").replace(/\\u2028/g, \"\\\\u2028\").replace(\n /\\u2029/g,\n \"\\\\u2029\",\n );\n}\nfunction qHash(q: string) {\n return q.toLowerCase().trim().replace(/\\s+/g, \" \").slice(0, 300);\n}\nfunction isNewsQuery(q: string) {\n const l = q.toLowerCase();\n return l.includes(\"חדשות\") || l.includes(\"היום\") || l.includes(\"news\");\n}\n\n// ========== DB ==========\nasync function dbInit() {\n try {\n await sqlite.execute(\n \"CREATE TABLE IF NOT EXISTS conversations (id INTEGER PRIMARY KEY AUTOINCREMENT, conversation_id TEXT NOT NULL, role TEXT NOT NULL, content TEXT NOT NULL, created_at INTEGER NOT NULL)\",\n );\n await sqlite.execute(\n \"CREATE INDEX IF NOT EXISTS idx_conv_id ON conversations(conversation_id)\",\n );\n await sqlite.execute(\n \"CREATE TABLE IF NOT EXISTS conv_meta (conversation_id TEXT PRIMARY KEY, title TEXT NOT NULL, created_at INTEGER NOT NULL, updated_at INTEGER NOT NULL)\",\n );\n await sqlite.execute(\n \"CREATE TABLE IF NOT EXISTS raw_answers (id INTEGER PRIMARY KEY AUTOINCREMENT, conversation_id TEXT NOT NULL, question TEXT NOT NULL, provider TEXT NOT NULL, provider_label TEXT NOT NULL, answer TEXT NOT NULL, created_at INTEGER NOT NULL)\",\n );\n await sqlite.execute(\n \"CREATE INDEX IF NOT EXISTS idx_raw_conv ON raw_answers(conversation_id)\",\n );\n await sqlite.execute(\n \"CREATE TABLE IF NOT EXISTS cache (qhash TEXT PRIMARY KEY, question TEXT NOT NULL, answer TEXT NOT NULL, raw_json TEXT NOT NULL, created_at INTEGER NOT NULL)\",\n );\n await sqlite.execute(\n \"CREATE TABLE IF NOT EXISTS provider_health (provider TEXT PRIMARY KEY, last_success INTEGER, last_failure INTEGER, failure_count INTEGER DEFAULT 0)\",\n );\n try {\n await sqlite.execute(\n \"ALTER TABLE conv_meta ADD COLUMN pinned INTEGER DEFAULT 0\",\n );\n } catch {}\n } catch {}\n}\nasync function dbHist(cid: string): Promise {\n if (!cid) return [];\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT role, content FROM conversations WHERE conversation_id =? ORDER BY created_at DESC LIMIT?\",\n args: [cid, MAX_HIST],\n });\n return (r.rows as any[] || []).map((\n row,\n ) => ({\n role: String(row.role) === \"user\" ? \"user\" : \"assistant\",\n content: String(row.content || \"\"),\n } as Msg)).filter((m) => m.content).reverse();\n } catch {\n return [];\n }\n}\nasync function dbSave(cid: string, role: string, content: string) {\n if (!cid || !content) return;\n try {\n await sqlite.execute({\n sql:\n \"INSERT INTO conversations (conversation_id, role, content, created_at) VALUES (?,?,?,?)\",\n args: [cid, role, content, Date.now()],\n });\n } catch {}\n}\nasync function dbSaveRaw(cid: string, question: string, raws: AIR[]) {\n if (!cid || !raws.length) return;\n const now = Date.now();\n try {\n for (const r of raws) {\n if (!r.a) continue;\n await sqlite.execute({\n sql:\n \"INSERT INTO raw_answers (conversation_id, question, provider, provider_label, answer, created_at) VALUES (?,?,?,?,?,?)\",\n args: [cid, question, r.pk, r.p, r.a, now],\n });\n }\n } catch {}\n}\nasync function dbGetRaw(cid: string) {\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT question, provider, provider_label, answer, created_at FROM raw_answers WHERE conversation_id =? ORDER BY created_at ASC\",\n args: [cid],\n });\n return (r.rows as any[] || []).map((row) => ({\n question: String(row.question || \"\"),\n provider: String(row.provider || \"\"),\n label: String(row.provider_label || \"\"),\n answer: String(row.answer || \"\"),\n ts: Number(row.created_at) || 0,\n }));\n } catch {\n return [];\n }\n}\nasync function getCache(qh: string) {\n try {\n const r = await sqlite.execute({\n sql: \"SELECT answer, raw_json, created_at FROM cache WHERE qhash=?\",\n args: [qh],\n });\n const row = (r.rows as any[])?.[0];\n if (!row) return null;\n if (Date.now() - Number(row.created_at) > CACHE_TTL) return null;\n return {\n answer: String(row.answer),\n raw: JSON.parse(String(row.raw_json || \"[]\")),\n };\n } catch {\n return null;\n }\n}\nasync function setCache(qh: string, q: string, answer: string, raw: AIR[]) {\n try {\n await sqlite.execute({\n sql:\n \"INSERT OR REPLACE INTO cache (qhash, question, answer, raw_json, created_at) VALUES (?,?,?,?,?)\",\n args: [qh, q, answer, JSON.stringify(raw), Date.now()],\n });\n } catch {}\n}\nasync function dbMeta(cid: string, title: string) {\n if (!cid) return;\n try {\n const ex = await sqlite.execute({\n sql: \"SELECT conversation_id FROM conv_meta WHERE conversation_id =?\",\n args: [cid],\n });\n const now = Date.now();\n const safeTitle = tr(title, 60) || \"שיחה חדשה\";\n if ((ex.rows as any[]).length === 0) {\n await sqlite.execute({\n sql:\n \"INSERT INTO conv_meta (conversation_id, title, created_at, updated_at, pinned) VALUES (?,?,?,?,?)\",\n args: [cid, safeTitle, now, now, 0],\n });\n } else {\n await sqlite.execute({\n sql:\n \"UPDATE conv_meta SET updated_at =?, title = COALESCE(title,?) WHERE conversation_id =?\",\n args: [now, safeTitle, cid],\n });\n }\n } catch {}\n}\nasync function dbList() {\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT conversation_id, title, updated_at, pinned FROM conv_meta ORDER BY pinned DESC, updated_at DESC LIMIT?\",\n args: [MAX_CONV],\n });\n return (r.rows as any[] || []).map((row) => ({\n id: String(row.conversation_id || \"\"),\n title: String(row.title || \"ללא כותרת\"),\n updated_at: Number(row.updated_at) || 0,\n pinned: Number(row.pinned) || 0,\n }));\n } catch {\n return [];\n }\n}\nasync function dbFull(cid: string): Promise {\n if (!cid) return [];\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT role, content, created_at FROM conversations WHERE conversation_id =? ORDER BY created_at ASC\",\n args: [cid],\n });\n return (r.rows as any[] || []).map((\n row,\n ) => ({\n role: String(row.role) === \"user\" ? \"user\" : \"assistant\",\n content: String(row.content || \"\"),\n ts: Number(row.created_at) || 0,\n } as Msg)).filter((m) => m.content);\n } catch {\n return [];\n }\n}\nasync function dbDel(cid: string) {\n if (!cid) return false;\n try {\n await sqlite.execute({\n sql: \"DELETE FROM conversations WHERE conversation_id =?\",\n args: [cid],\n });\n await sqlite.execute({\n sql: \"DELETE FROM conv_meta WHERE conversation_id =?\",\n args: [cid],\n });\n await sqlite.execute({\n sql: \"DELETE FROM raw_answers WHERE conversation_id =?\",\n args: [cid],\n });\n return true;\n } catch {\n return false;\n }\n}\nasync function dbRen(cid: string, title: string) {\n if (!cid || !title) return false;\n try {\n await sqlite.execute({\n sql:\n \"UPDATE conv_meta SET title =?, updated_at =? WHERE conversation_id =?\",\n args: [tr(title, 80), Date.now(), cid],\n });\n return true;\n } catch {\n return false;\n }\n}\nasync function dbHealth(provider: string, success: boolean) {\n try {\n const now = Date.now();\n const ex = await sqlite.execute({\n sql: \"SELECT provider FROM provider_health WHERE provider =?\",\n args: [provider],\n });\n if ((ex.rows as any[]).length === 0) {\n await sqlite.execute({\n sql:\n \"INSERT INTO provider_health (provider, last_success, last_failure, failure_count) VALUES (?,?,?,?)\",\n args: [provider, success ? now : 0, success ? 0 : now, success ? 0 : 1],\n });\n } else if (success) {\n await sqlite.execute({\n sql:\n \"UPDATE provider_health SET last_success =?, failure_count = 0 WHERE provider =?\",\n args: [now, provider],\n });\n } else {\n await sqlite.execute({\n sql:\n \"UPDATE provider_health SET last_failure =?, failure_count = failure_count + 1 WHERE provider =?\",\n args: [now, provider],\n });\n }\n } catch {}\n}\nasync function dbGetHealth() {\n try {\n const r = await sqlite.execute({\n sql:\n \"SELECT provider, last_success, last_failure, failure_count FROM provider_health\",\n });\n return (r.rows as any[] || []).map((row) => ({\n provider: String(row.provider),\n last_success: Number(row.last_success) || 0,\n last_failure: Number(row.last_failure) || 0,\n failure_count: Number(row.failure_count) || 0,\n }));\n } catch {\n return [];\n }\n}\n\n// ========== TITLE GENERATOR (בשימוש!) ==========\nasync function generateTitle(q: string, k: string): Promise {\n if (!k) return tr(q, 40);\n try {\n const url =\n \"https://generativelanguage.googleapis.com/v1beta/models/gemini-flash-lite-latest:generateContent?key=\" +\n encodeURIComponent(k);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{\n parts: [{ text: 'כותרת 3-4 מילים בעברית: \"' + tr(q, 200) + '\"' }],\n }],\n generationConfig: { temperature: 0.3, maxOutputTokens: 20 },\n }),\n }, 0);\n if (!r.ok) return tr(q, 40);\n const d = await r.json();\n return d?.candidates?.[0]?.content?.parts?.[0]?.text?.trim().replace(\n /[\"']/g,\n \"\",\n ).slice(0, 40) || tr(q, 40);\n } catch {\n return tr(q, 40);\n }\n}\n\n// ========== CONTEXT / SEARCH / TRANSLATE ==========\nfunction ctx(hist: Msg[], q: string) {\n if (hist.length === 0) return q;\n return tr(\n \"Previous:\\n\" + hist.map((m) =>\n (m.role === \"user\" ? \"User: \" : \"Assistant: \") + m.content\n ).join(\"\\n\") + \"\\n\\nQuestion: \" + q,\n 15000,\n );\n}\nasync function webSearch(q: string, k: string) {\n if (!k || !q) return \"\";\n try {\n const today = new Date().toISOString().split(\"T\")[0];\n const searchQ = isNewsQuery(q) ? q + \" \" + today + \" Israel\" : q;\n const r = await fetch(\"https://api.tavily.com/search\", {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n api_key: k,\n query: searchQ,\n max_results: 8,\n search_depth: \"advanced\",\n time_range: isNewsQuery(q) ? \"day\" : undefined,\n include_answer: true,\n }),\n });\n if (!r.ok) return \"\";\n const d = await r.json();\n return (d.results as any[] || []).map((x) =>\n \"Source: \" + x.title + \"\\n\" + x.content + \"\\nURL: \" + x.url\n ).join(\"\\n\\n\") + (d.answer ? \"\\n\\nSummary: \" + d.answer : \"\");\n } catch {\n return \"\";\n }\n}\nasync function translateToEnglish(p: string, k: string): Promise {\n if (!k || !p) return p;\n if (/^[a-zA-Z0-9,.'\"\\-\\s]+$/.test(p) && p.length < 100) return p;\n try {\n const url =\n \"https://generativelanguage.googleapis.com/v1beta/models/gemini-flash-lite-latest:generateContent?key=\" +\n encodeURIComponent(k);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{\n parts: [{\n text: 'Translate to English FLUX prompt, ONLY English: \"' + p + '\"',\n }],\n }],\n generationConfig: { temperature: 0.2, maxOutputTokens: 200 },\n }),\n }, 0);\n if (!r.ok) return p;\n const d = await r.json();\n return d?.candidates?.[0]?.content?.parts?.[0]?.text?.trim().replace(\n /^\"|\"$/g,\n \"\",\n ).slice(0, 400) || p;\n } catch {\n return p;\n }\n}\n\n// ========== POLLINATIONS CATALOG ==========\nlet pollinationsCatalog: any[] | null = null;\nlet aliasToCanonical = new Map();\nlet canonicalToMeta = new Map();\nasync function fetchPollinationsCatalog(): Promise {\n if (pollinationsCatalog) return pollinationsCatalog;\n try {\n const r = await fetch(POLLINATIONS_BASE + \"/models\");\n if (!r.ok) return [];\n const data = await r.json();\n const list = Array.isArray(data)\n ? data\n : (data?.models || data?.data || []);\n pollinationsCatalog = list;\n aliasToCanonical.clear();\n canonicalToMeta.clear();\n for (const m of list) {\n const canonical = m.id || m.name || \"\";\n if (!canonical) continue;\n const meta = {\n id: canonical,\n publisher: m.publisher || m.brand || canonical.split(\"/\")[0],\n brand_url: m.brand_url || \"\",\n aliases: m.aliases || [],\n ...m,\n };\n canonicalToMeta.set(canonical, meta);\n aliasToCanonical.set(canonical, canonical);\n for (const alias of (m.aliases || [])) {\n aliasToCanonical.set(alias, canonical);\n }\n const short = canonical.split(\"/\").pop()!;\n if (!aliasToCanonical.has(short)) aliasToCanonical.set(short, canonical);\n }\n return list;\n } catch {\n return [];\n }\n}\nfunction resolveModelId(input: string): string {\n if (aliasToCanonical.has(input)) return aliasToCanonical.get(input)!;\n const low = input.toLowerCase();\n for (const [k, v] of aliasToCanonical.entries()) {\n if (k.toLowerCase() === low) return v;\n }\n return input;\n}\nfunction getModelMeta(id: string) {\n const canonical = resolveModelId(id);\n return canonicalToMeta.get(canonical) ||\n { id: canonical, publisher: canonical.split(\"/\")[0], brand_url: \"\" };\n}\n\n// ========== IMAGE GENERATION ==========\nasync function genImage(\n promptEn: string,\n acc: string,\n tok: string,\n style: string = \"photorealistic\",\n quality: string = \"hd\",\n geminiKey: string = \"\",\n pollinationsKey: string = \"\",\n): Promise {\n const pk = \"image\", p = \"🎨 ULTRA\";\n const seed = Math.floor(Math.random() * 999999) + 1;\n let width = 1280, height = 1280, steps = 20;\n if (quality === \"ultra\") {\n width = 1536;\n height = 1536;\n steps = 30;\n } else if (quality === \"draft\") {\n width = 768;\n height = 768;\n steps = 12;\n }\n\n let suffix =\n \", ultra detailed, 8k, masterpiece, photorealistic, cinematic lighting, sharp focus, best quality\";\n if (style === \"anime\") suffix = \", anime style, vibrant, 8k\";\n else if (style === \"3d\") suffix = \", 3d render, octane, 8k\";\n else if (style === \"fast\") suffix = \", quick sketch, minimal\";\n const enhanced = promptEn + suffix;\n const enc = encodeURIComponent(enhanced);\n\n // 1. Nano Banana (Gemini image) - ראשי\n if (geminiKey) {\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n NANO_BANANA + \":generateContent?key=\" + encodeURIComponent(geminiKey);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n contents: [{ parts: [{ text: enhanced }] }],\n generationConfig: { responseModalities: [\"IMAGE\", \"TEXT\"] },\n }),\n }, 0);\n if (r.ok) {\n const d = await r.json();\n const parts = d?.candidates?.[0]?.content?.parts || [];\n for (const part of parts) {\n if (part.inlineData && part.inlineData.data) {\n return {\n p: \"🍌 ננו-בננה\",\n pk,\n a: \"![תמונה](data:\" + (part.inlineData.mimeType || \"image/png\") +\n \";base64,\" + part.inlineData.data + \")\",\n ms: 0,\n };\n }\n }\n }\n } catch {}\n }\n\n // 2. Pollinations API (אם יש מפתח)\n if (pollinationsKey) {\n try {\n await fetchPollinationsCatalog();\n let desiredModel = \"black-forest-labs/flux.2-pro\";\n if (quality === \"ultra\") desiredModel = \"google/gemini-3-pro-image\";\n else if (quality === \"draft\") {\n desiredModel = \"black-forest-labs/flux.1-schnell\";\n }\n const canonical = resolveModelId(desiredModel);\n const meta = getModelMeta(canonical);\n const r = await fetch(POLLINATIONS_BASE + \"/v1/images/generations\", {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + pollinationsKey,\n },\n body: JSON.stringify({\n model: canonical,\n prompt: enhanced,\n width,\n height,\n n: 1,\n response_format: \"url\",\n }),\n });\n if (r.ok) {\n const d = await r.json();\n const imgUrl = d?.data?.[0]?.url ||\n (d?.data?.[0]?.b64_json\n ? `data:image/png;base64,${d.data[0].b64_json}`\n : \"\");\n if (imgUrl) {\n return {\n p: `🎨 ${meta.publisher} ${canonical.split(\"/\").pop()}`,\n pk,\n a: `![תמונה](${imgUrl})`,\n ms: 0,\n };\n }\n }\n } catch {}\n }\n\n // 3. Cloudflare FLUX\n if (acc && tok) {\n try {\n const url = \"https://api.cloudflare.com/client/v4/accounts/\" +\n encodeURIComponent(acc) + \"/ai/run/\" + CF_IMAGE_MODEL;\n const r = await fr(url, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + tok,\n },\n body: JSON.stringify({\n prompt: enhanced,\n num_steps: steps,\n guidance: 7.5,\n }),\n }, 0);\n if (r.ok) {\n const buf = await r.arrayBuffer();\n if (buf.byteLength > 5000) {\n const bytes = new Uint8Array(buf);\n let binary = \"\";\n const chunk = 8192;\n for (let i = 0; i < bytes.length; i += chunk) {\n binary += String.fromCharCode.apply(\n null,\n Array.from(bytes.subarray(i, i + chunk)) as any,\n );\n }\n return {\n p,\n pk,\n a: \"![תמונה](data:image/png;base64,\" + btoa(binary) + \")\",\n ms: 0,\n };\n }\n }\n } catch {}\n }\n\n // 4. Pollinations URL fallback\n const proUrl = \"https://image.pollinations.ai/prompt/\" + enc + \"?width=\" +\n width + \"&height=\" + height +\n \"&model=flux-pro&enhance=true&nologo=true&seed=\" + seed;\n return { p, pk, a: \"![תמונה](\" + proUrl + \")\" };\n}\n\n// ========== PROVIDER CALLS ==========\nasync function askGemini(prompt: string, k: string): Promise {\n const pk = \"gemini\", p = PL[pk];\n if (!k) return { p, pk, a: \"\", e: \"לא מוגדר\" };\n const s = Date.now();\n return wt(\n (async () => {\n try {\n const url = \"https://generativelanguage.googleapis.com/v1beta/models/\" +\n MODELS.gemini + \":generateContent?key=\" + encodeURIComponent(k);\n const r = await fr(url, {\n method: \"POST\",\n headers: { \"Content-Type\": \"application/json\" },\n body: JSON.stringify({\n systemInstruction: { parts: [{ text: ANON }] },\n contents: [{ parts: [{ text: prompt }] }],\n generationConfig: { temperature: 0.7, maxOutputTokens: 8000 },\n }),\n });\n if (!r.ok) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: \"שגיאה \" + r.status, ms: Date.now() - s };\n }\n const d = await r.json();\n const a = d?.candidates?.[0]?.content?.parts?.[0]?.text?.trim() || \"\";\n if (!a) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: \"ריק\", ms: Date.now() - s };\n }\n await dbHealth(pk, true);\n return { p, pk, a, ms: Date.now() - s, ti: et(prompt), to: et(a) };\n } catch (e) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: String(e).slice(0, 120), ms: Date.now() - s };\n }\n })(),\n P_MS,\n { p, pk, a: \"\", e: \"פסק זמן\", ms: 0 },\n );\n}\nasync function askCompat(\n prompt: string,\n k: string,\n pk: string,\n endpoint: string,\n model: string,\n): Promise {\n const p = PL[pk];\n if (!k) return { p, pk, a: \"\", e: \"לא מוגדר\" };\n const s = Date.now();\n return wt(\n (async () => {\n try {\n const h: any = {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + k,\n };\n if (pk === \"openrouter\") {\n h[\"HTTP-Referer\"] = \"https://multi-ai-chat.com\";\n h[\"X-Title\"] = \"Multi-AI\";\n }\n const r = await fr(endpoint, {\n method: \"POST\",\n headers: h,\n body: JSON.stringify({\n model,\n messages: [{ role: \"system\", content: ANON }, {\n role: \"user\",\n content: prompt,\n }],\n temperature: 0.7,\n max_tokens: 8000,\n }),\n });\n if (!r.ok) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: \"שגיאה \" + r.status, ms: Date.now() - s };\n }\n const d = await r.json();\n const a = d?.choices?.[0]?.message?.content?.trim() || \"\";\n if (!a) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: \"ריק\", ms: Date.now() - s };\n }\n await dbHealth(pk, true);\n return { p, pk, a, ms: Date.now() - s, ti: et(prompt), to: et(a) };\n } catch (e) {\n await dbHealth(pk, false);\n return { p, pk, a: \"\", e: String(e).slice(0, 120), ms: Date.now() - s };\n }\n })(),\n P_MS,\n { p, pk, a: \"\", e: \"פסק זמן\", ms: 0 },\n );\n}\nasync function askGroqDual(\n prompt: string,\n k1: string,\n k2: string,\n): Promise {\n const pk = \"groq\", p = PL[pk];\n if (!k1 && !k2) return { p, pk, a: \"\", e: \"לא מוגדר\" };\n const eps = \"https://api.groq.com/openai/v1/chat/completions\";\n if (k1) {\n const r1 = await askCompat(prompt, k1, pk, eps, MODELS[pk]);\n if (r1.a && !r1.e) return r1;\n if (r1.e && (r1.e.includes(\"429\") || r1.e.includes(\"402\")) && k2) {\n const r2 = await askCompat(prompt, k2, pk, eps, MODELS[pk]);\n if (r2.a) {\n r2.p = PL[pk] + \" (key2)\";\n return r2;\n }\n }\n return r1;\n }\n return askCompat(prompt, k2, pk, eps, MODELS[pk]);\n}\nasync function askProvider(\n prompt: string,\n keys: any,\n pk: string,\n): Promise {\n if (pk === \"gemini\") return askGemini(prompt, keys.gemini);\n if (pk === \"groq\") return askGroqDual(prompt, keys.groq, keys.groq2);\n const eps: Record = {\n cerebras: \"https://api.cerebras.ai/v1/chat/completions\",\n nvidia: \"https://integrate.api.nvidia.com/v1/chat/completions\",\n mistral: \"https://api.mistral.ai/v1/chat/completions\",\n openrouter: \"https://openrouter.ai/api/v1/chat/completions\",\n cohere: \"https://api.cohere.ai/compatibility/v1/chat/completions\",\n zai: \"https://open.bigmodel.cn/api/paas/v4/chat/completions\",\n dahl: \"https://inference.dahl.global/v1/chat/completions\",\n };\n return askCompat(prompt, keys[pk], pk, eps[pk], MODELS[pk]);\n}\nasync function cfCall(\n messages: any[],\n acc: string,\n tok: string,\n): Promise {\n try {\n const url = \"https://api.cloudflare.com/client/v4/accounts/\" +\n encodeURIComponent(acc) + \"/ai/run/\" + CF_MODEL;\n const r = await fr(url, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\",\n Authorization: \"Bearer \" + tok,\n },\n body: JSON.stringify({ messages, max_tokens: 8000 }),\n });\n if (!r.ok) return \"\";\n const d = await r.json();\n return String(d?.result?.response || \"\").trim();\n } catch {\n return \"\";\n }\n}\n\n// ========== JUDGE PROMPT ==========\nfunction buildJP(q: string, answers: AIR[], hist: Msg[], searchCtx: string) {\n const valid = answers.filter((a) =>\n a.a && a.a.length > 20 && !a.a.includes(\"המערכת פותחה על ידי מוטי כץ\")\n );\n const fb = valid.length === 0\n ? answers.filter((a) => a.a && a.a.length > 0)\n : valid;\n const txt = fb.map((a) => \"--- DRAFT ---\\n\" + tr(a.a, MAX_JA)).join(\"\\n\\n\");\n const histTxt = hist.length\n ? \"History:\\n\" + hist.map((m) =>\n (m.role === \"user\" ? \"User: \" : \"Assistant: \") + tr(m.content, 500)\n ).join(\"\\n\") + \"\\n\\n\"\n : \"\";\n const searchBlock = searchCtx\n ? \"\\n\\nWEB SEARCH (FRESH \" + new Date().toLocaleDateString(\"he-IL\") +\n \"):\\n\" + tr(searchCtx, 5000) + \"\\n\"\n : \"\";\n return JSP + \"\\n\\n\" + histTxt + \"User Question: \" + q + \"\\n\" + searchBlock +\n \"\\nDrafts:\\n\" + txt + \"\\n\\nFinal answer:\";\n}\nfunction parseJP(out: string) {\n const s = String(out || \"\");\n const a = s.match(/ANSWER:\\s*([\\s\\S]*)/i);\n return { answer: a ? a[1].trim() : s.trim() };\n}\n\n// ========== PAGE ==========\nfunction page(\n cid: string,\n convs: any[],\n msgs: Msg[],\n avail: string[],\n health: any[],\n): Response {\n const html =\n `Multi-AI v29 PERFECT

שיחה חדשה

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\n\n\n\n`;\n return new Response(html, {\n headers: {\n \"Content-Type\": \"text/html; charset=utf-8\",\n \"Cache-Control\": \"no-store\",\n },\n });\n}\n\n// ========== MAIN HANDLER ==========\nexport default async function (req: Request): Promise {\n const url = new URL(req.url);\n const GEMINI_API_KEY = Deno.env.get(\"GEMINI_API_KEY\") || \"\";\n const GROQ_API_KEY = Deno.env.get(\"GROQ_API_KEY\") || \"\";\n const GROQ_API_KEY_2 = Deno.env.get(\"GROQ_API_KEY_2\") || \"\";\n const CEREBRAS_API_KEY = Deno.env.get(\"CEREBRAS_API_KEY\") || \"\";\n const NVIDIA_API_KEY = Deno.env.get(\"NVIDIA_API_KEY\") || \"\";\n const MISTRAL_API_KEY = Deno.env.get(\"MISTRAL_API_KEY\") || \"\";\n const OPENROUTER_API_KEY = Deno.env.get(\"OPENROUTER_API_KEY\") || \"\";\n const COHERE_API_KEY = Deno.env.get(\"COHERE_API_KEY\") || \"\";\n const ZAI_API_KEY = Deno.env.get(\"ZAI_API_KEY\") || \"\";\n const DAHL_API_KEY = Deno.env.get(\"DAHL_API_KEY\") || \"\";\n const CLOUDFLARE_ACCOUNT_ID = Deno.env.get(\"CLOUDFLARE_ACCOUNT_ID\") || \"\";\n const CLOUDFLARE_API_TOKEN = Deno.env.get(\"CLOUDFLARE_API_TOKEN\") || \"\";\n const TAVILY_API_KEY = Deno.env.get(\"TAVILY_API_KEY\") || \"\";\n const POLLINATIONS_API_KEY = Deno.env.get(\"POLLINATIONS_API_KEY\") ||\n Deno.env.get(\"POLLINATIONS_KEY\") || \"\";\n await dbInit();\n\n // --- DELETE ---\n if (req.method === \"DELETE\" && url.searchParams.get(\"action\") === \"delete\") {\n const ok = await dbDel(url.searchParams.get(\"c\") || \"\");\n return new Response(JSON.stringify({ ok }), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n // --- RENAME ---\n if (req.method === \"POST\" && url.searchParams.get(\"action\") === \"rename\") {\n try {\n const fd = await req.formData();\n const ok = await dbRen(\n String(fd.get(\"id\") || \"\"),\n String(fd.get(\"title\") || \"\"),\n );\n return new Response(JSON.stringify({ ok }), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n } catch {\n return new Response(JSON.stringify({ ok: false }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n }\n // --- EXPORT FULL ---\n if (\n req.method === \"GET\" && url.searchParams.get(\"action\") === \"export_full\"\n ) {\n const cid = url.searchParams.get(\"c\") || \"\";\n if (!cid) return new Response(\"חסר c\", { status: 400 });\n const full = await dbFull(cid);\n const raws = await dbGetRaw(cid);\n let md = \"# שיחה מלאה - v29 PERFECT\\n\\n---\\n\\n\";\n const questions = full.filter((m) => m.role === \"user\");\n for (let i = 0; i < questions.length; i++) {\n const q = questions[i];\n md += \"## 👤 שאלה \" + (i + 1) + \"\\n\\n\" + q.content.slice(0, 50000) +\n \"\\n\\n\";\n const relatedRaw = raws.filter((r) => r.question === q.content);\n for (const r of relatedRaw) {\n md += \"### \" + r.label + \" (\" + r.provider + \")\\n\\n\" +\n r.answer.slice(0, 50000) + \"\\n\\n\";\n }\n const nextUserIdx = full.findIndex((m, idx) =>\n idx > full.indexOf(q) && m.role === \"user\"\n );\n const finalAns = full.slice(\n full.indexOf(q) + 1,\n nextUserIdx === -1 ? undefined : nextUserIdx,\n ).find((m) => m.role === \"assistant\");\n if (finalAns) {\n md += \"### 🤖 תשובה סופית\\n\\n\" + finalAns.content.slice(0, 50000) +\n \"\\n\\n---\\n\\n\";\n }\n }\n return new Response(md, {\n headers: {\n \"Content-Type\": \"text/markdown; charset=utf-8\",\n \"Content-Disposition\": \"attachment; filename=chat-full-\" + Date.now() +\n \".md\",\n },\n });\n }\n // --- EXPORT JSON ---\n if (\n req.method === \"GET\" && url.searchParams.get(\"action\") === \"export_json\"\n ) {\n const cid = url.searchParams.get(\"c\") || \"\";\n if (!cid) return new Response(\"חסר c\", { status: 400 });\n const full = await dbFull(cid);\n const raws = await dbGetRaw(cid);\n return new Response(\n JSON.stringify(\n {\n conversation_id: cid,\n messages: full,\n raw_answers: raws,\n exported_at: Date.now(),\n version: \"v29\",\n },\n null,\n 2,\n ),\n {\n headers: {\n \"Content-Type\": \"application/json; charset=utf-8\",\n \"Content-Disposition\": \"attachment; filename=chat-\" + Date.now() +\n \".json\",\n },\n },\n );\n }\n // --- POLLINATIONS MODELS ---\n if (\n req.method === \"GET\" &&\n url.searchParams.get(\"action\") === \"pollinations_models\"\n ) {\n const catalog = await fetchPollinationsCatalog();\n return new Response(JSON.stringify(catalog), {\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n // --- POST ---\n if (req.method === \"POST\") {\n let q = \"\", cid = \"\", prov = \"\", fileText = \"\";\n let forceSearchParam = \"auto\", forceRecalcParam = false;\n let imgStyle = \"photorealistic\", imgQuality = \"hd\";\n try {\n const fd = await req.formData();\n q = String(fd.get(\"q\") || \"\").trim();\n cid = String(fd.get(\"c\") || \"\");\n prov = String(fd.get(\"p\") || \"\");\n forceSearchParam = String(fd.get(\"forceSearch\") || \"auto\");\n forceRecalcParam = String(fd.get(\"forceRecalc\") || \"\") === \"true\";\n imgStyle = String(fd.get(\"imgStyle\") || \"photorealistic\");\n imgQuality = String(fd.get(\"imgQuality\") || \"hd\");\n for (const [key, val] of fd.entries()) {\n if (key.startsWith(\"file_\") && val instanceof File) {\n try {\n fileText += \"\\n\\n[קובץ: \" + val.name + \"]\\n\" +\n (await val.text()).slice(0, 20000) + \"\\n\";\n } catch {}\n }\n }\n } catch {\n return new Response(JSON.stringify({ error: \"בקשה לא תקינה\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n if (fileText) q = (q + \"\\n\\n--- קבצים ---\\n\" + fileText).slice(0, MAX_Q);\n if (!q) {\n return new Response(JSON.stringify({ error: \"חסרה שאלה\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n if (q.length > MAX_Q) {\n return new Response(JSON.stringify({ error: \"ארוכה מדי\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n if (!cid) {\n cid = \"conv_\" + Date.now() + \"_\" + Math.random().toString(36).slice(2, 6);\n }\n if (!checkRateLimit(cid)) {\n return new Response(JSON.stringify({ error: \"יותר מדי בקשות\" }), {\n status: 429,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n const trimmedQ = q.trim().toLowerCase();\n const isImageReq = trimmedQ.startsWith(\"/image \") ||\n trimmedQ.startsWith(\"/תמונה \") || trimmedQ.startsWith(\"צייר לי \") ||\n trimmedQ.startsWith(\"🎨\") ||\n /^\\[(photorealistic|anime|3d|fast|draft|hd|ultra)\\]/i.test(trimmedQ);\n\n if (isImageReq) {\n let rawPrompt = q.replace(/\\/image\\s*/i, \"\").replace(/\\/תמונה\\s*/i, \"\")\n .replace(/צייר לי\\s*/i, \"\").replace(/🎨/g, \"\").trim();\n const allBrackets = [...rawPrompt.matchAll(/\\[([a-z0-9-]+)\\]/gi)].map((\n m,\n ) => m[1].toLowerCase());\n let styleFromQ = imgStyle, qualityFromQ = imgQuality;\n for (const b of allBrackets) {\n if ([\"photorealistic\", \"anime\", \"3d\", \"fast\"].includes(b)) {\n styleFromQ = b;\n }\n if ([\"draft\", \"hd\", \"ultra\"].includes(b)) qualityFromQ = b;\n }\n rawPrompt = rawPrompt.replace(/\\[.*?\\]/g, \"\").trim();\n if (!rawPrompt) rawPrompt = \"beautiful landscape\";\n const enPrompt = await translateToEnglish(rawPrompt, GEMINI_API_KEY);\n const imgRes = await genImage(\n enPrompt,\n CLOUDFLARE_ACCOUNT_ID,\n CLOUDFLARE_API_TOKEN,\n styleFromQ,\n qualityFromQ,\n GEMINI_API_KEY,\n POLLINATIONS_API_KEY,\n );\n await dbSave(cid, \"user\", q);\n await dbSave(cid, \"assistant\", imgRes.a);\n const t = await generateTitle(q, GEMINI_API_KEY);\n await dbMeta(cid, t);\n return new Response(\n JSON.stringify({\n answer: imgRes.a,\n raw: [imgRes],\n memory: 0,\n search: false,\n direct: true,\n totalTokens: 0,\n conversationId: cid,\n }),\n { headers: { \"Content-Type\": \"application/json; charset=utf-8\" } },\n );\n }\n\n const qh = qHash(q);\n const skipCache = isNewsQuery(q) || forceSearchParam === \"true\" ||\n forceRecalcParam;\n if (!skipCache) {\n const cached = await getCache(qh);\n if (cached) {\n await dbSave(cid, \"user\", q);\n await dbSave(cid, \"assistant\", cached.answer);\n const t = await generateTitle(q, GEMINI_API_KEY);\n await dbMeta(cid, t);\n return new Response(\n JSON.stringify({\n answer: cached.answer,\n raw: cached.raw,\n memory: 0,\n search: false,\n direct: false,\n totalTokens: 0,\n conversationId: cid,\n cached: true,\n }),\n { headers: { \"Content-Type\": \"application/json; charset=utf-8\" } },\n );\n }\n }\n\n let enabled = prov\n ? prov.split(\",\").map((s) => s.trim()).filter((s) => ALL.includes(s))\n : ALL.slice();\n const keys: any = {\n gemini: GEMINI_API_KEY,\n groq: GROQ_API_KEY,\n groq2: GROQ_API_KEY_2,\n cerebras: CEREBRAS_API_KEY,\n nvidia: NVIDIA_API_KEY,\n mistral: MISTRAL_API_KEY,\n openrouter: OPENROUTER_API_KEY,\n cohere: COHERE_API_KEY,\n zai: ZAI_API_KEY,\n dahl: DAHL_API_KEY,\n };\n enabled = enabled.filter((p) =>\n keys[p] || (p === \"groq\" && (keys.groq || keys.groq2))\n );\n if (enabled.length === 0) {\n return new Response(JSON.stringify({ error: \"אין מנועים\" }), {\n status: 400,\n headers: { \"Content-Type\": \"application/json\" },\n });\n }\n\n const hist = await dbHist(cid);\n let shouldSearch = false;\n if (TAVILY_API_KEY) {\n if (forceSearchParam === \"true\") shouldSearch = true;\n else if (forceSearchParam === \"false\") shouldSearch = isNewsQuery(q);\n else shouldSearch = isNewsQuery(q) || q.length > 10;\n }\n const searchCtx = shouldSearch\n ? await wt(webSearch(q, TAVILY_API_KEY), 7000, \"\")\n : \"\";\n const fullPrompt = ctx(hist, q) +\n (searchCtx ? \"\\n\\nWeb search results:\\n\" + searchCtx : \"\");\n const round1 = await Promise.all(\n enabled.map((pk) => askProvider(fullPrompt, keys, pk)),\n );\n\n let finalAnswer = \"\";\n const valid = round1.filter((a) =>\n a.a && a.a.length > 20 && !a.a.includes(\"המערכת פותחה על ידי מוטי כץ\")\n );\n const fb = valid.length > 0\n ? valid\n : round1.filter((a) => a.a && a.a.length > 0);\n if (fb.length === 0) {\n if (searchCtx) {\n const jpF = buildJP(q, [], hist, searchCtx);\n let jrF = await wt(\n cfCall(\n [{ role: \"system\", content: JSP }, { role: \"user\", content: jpF }],\n CLOUDFLARE_ACCOUNT_ID,\n CLOUDFLARE_API_TOKEN,\n ),\n J_MS,\n \"\",\n );\n if (!jrF && GEMINI_API_KEY) {\n const g = await askGemini(jpF, GEMINI_API_KEY);\n jrF = g.a;\n }\n finalAnswer = parseJP(jrF || \"\").answer || searchCtx.slice(0, 3000);\n } else finalAnswer = \"כל המנועים נכשלו. נסה שוב.\";\n } else if (fb.length === 1) finalAnswer = fb[0].a;\n else {\n const jp = buildJP(q, round1, hist, searchCtx);\n let jr = await wt(\n cfCall(\n [{ role: \"system\", content: JSP }, { role: \"user\", content: jp }],\n CLOUDFLARE_ACCOUNT_ID,\n CLOUDFLARE_API_TOKEN,\n ),\n J_MS,\n \"\",\n );\n if (!jr && GEMINI_API_KEY) {\n const g = await askGemini(jp, GEMINI_API_KEY);\n jr = g.a;\n }\n finalAnswer = parseJP(jr || \"\").answer || fb[0].a;\n }\n await dbSave(cid, \"user\", q);\n await dbSaveRaw(cid, q, round1.filter((r) => r.a));\n await dbSave(cid, \"assistant\", finalAnswer);\n const title = await generateTitle(q, GEMINI_API_KEY);\n await dbMeta(cid, title);\n if (!isNewsQuery(q)) await setCache(qh, q, finalAnswer, round1);\n const totalTokens = round1.reduce(\n (s, a) => s + (a.ti || 0) + (a.to || 0),\n 0,\n );\n return new Response(\n JSON.stringify({\n answer: finalAnswer,\n raw: round1,\n memory: hist.length,\n search: !!searchCtx,\n searchContent: searchCtx,\n direct: fb.length === 1,\n totalTokens,\n conversationId: cid,\n cached: false,\n }),\n { headers: { \"Content-Type\": \"application/json; charset=utf-8\" } },\n );\n }\n\n // --- GET (Page) ---\n const cid = url.searchParams.get(\"c\") || \"\";\n const convs = await dbList();\n const msgs = cid ? await dbFull(cid) : [];\n const health = await dbGetHealth();\n const avail: string[] = [];\n if (GEMINI_API_KEY) avail.push(\"gemini\");\n if (GROQ_API_KEY || GROQ_API_KEY_2) avail.push(\"groq\");\n if (CEREBRAS_API_KEY) avail.push(\"cerebras\");\n if (NVIDIA_API_KEY) avail.push(\"nvidia\");\n if (MISTRAL_API_KEY) avail.push(\"mistral\");\n if (OPENROUTER_API_KEY) avail.push(\"openrouter\");\n if (COHERE_API_KEY) avail.push(\"cohere\");\n if (ZAI_API_KEY) avail.push(\"zai\");\n if (DAHL_API_KEY) avail.push(\"dahl\");\n return page(cid, convs, msgs, avail, health);\n}","deleted",false,"label",[14],"29982844-42bc-4943-abe2-b65cce660249",["D",1789494982975],"deletedAt","legacyEnvVars","privacy","public","httpPrivacy","description","Multi-AI: 2 AI providers + Judge synthesis","imageUrl","userId","de465c43-a98f-45bf-a0fb-0b3e6dbf4bfd","pinnedFileId","pinPreview","pinMigrated","screenshotVersion","likeCount",0,"orgId","01a06b69-abd6-735e-bc5f-eadfefe0089d","tursoName","2998284442bc4943abe2b65cce660249","tursoHostname","2998284442bc4943abe2b65cce660249-val-town.aws-us-east-2.turso.io","tursoMeasuredAt",["D",1789522252250],"pinned","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm","user",{"_9":10,"_104":100},"org",{"_9":10,"_97":98,"_99":100,"_55":56,"_101":102,"_103":42},"branch",{"_35":30,"_11":88,"_22":28,"_89":45,"_90":-5,"_91":-5,"_92":-5,"_93":-5,"_94":-5,"_95":12,"_96":10},"isOwned","main","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","displayName","מוטי כץ","avatar","","tier","free","canRestrictVals","profileImageUrl","ctx",{"_81":-5,"_138":139,"_140":141,"_142":143,"_144":42,"_145":42,"_146":42,"_147":32,"_148":-5,"_149":-7,"_150":-7,"_151":62,"_152":42,"_153":42,"_154":-5,"_155":42,"_156":42,"_157":42,"_158":-5},"ENV",{"_109":110,"_111":112,"_113":88,"_114":115,"_116":117,"_118":119,"_120":121,"_122":123,"_124":125,"_126":127,"_128":129,"_130":131,"_132":133,"_134":135,"_136":137},"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","orgs",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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