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Nexuses Core OS V10.0 — Universal AI Gateway\n// HTTP Entry Point with IAP, JSON-RPC, strict auth, and Dynamic Request Normalizer\n\nimport {\n callAdapterDirect,\n dispatchTask,\n getHistory,\n getSystemStatus,\n getTaskStatus,\n} from \"./TaskDispatcher.ts\";\nimport { getAgentManifest } from \"./AgentManager.ts\";\nimport { listAdapters } from \"./UniversalAdapter.ts\";\nimport { getModelManifest } from \"./hf.ts\";\nimport { initDB } from \"./db.ts\";\nimport { buildCapabilityList, getServiceManifest } from \"./RegistryManager.ts\";\nimport { processHandshake, processRPC } from \"./IAP.ts\";\nimport type { JSONRPCRequest } from \"./IAP.ts\";\n\nconst CORS = {\n \"Content-Type\": \"application/json\",\n \"Access-Control-Allow-Origin\": \"*\",\n \"Access-Control-Allow-Methods\": \"GET, POST, OPTIONS\",\n \"Access-Control-Allow-Headers\": \"Content-Type, Authorization\",\n};\n\nfunction json(body: any, status = 200) {\n return new Response(JSON.stringify(body, null, 2), { status, headers: CORS });\n}\n\nfunction getEnv(key: string): string | undefined {\n try {\n return Deno.env.get(key);\n } catch {\n return undefined;\n }\n}\n\nfunction getEndpoint(req: Request): string {\n const url = new URL(req.url);\n return `${url.protocol}//${url.host}/`;\n}\n\n// --- Universal Request Normalizer ---\n// خود بخود ریکویسٹ کو پہچان کر فارمیٹ سیٹ کرتا ہے\nfunction normalizeRequest(payload: any) {\n const possibleTaskKeys = [\n \"task\",\n \"query\",\n \"prompt\",\n \"command\",\n \"message\",\n \"input\",\n \"body\",\n \"text\",\n ];\n let task = \"general\";\n let data = { ...payload };\n\n for (const key of possibleTaskKeys) {\n if (payload[key]) {\n task = payload[key];\n delete data[key];\n break;\n }\n }\n return { task, data };\n}\n\n// --- Auth Validation ---\nfunction validateAuth(req: Request): boolean {\n const apiKey = getEnv(\"NEXUSES_API_KEY\");\n if (!apiKey) return true; // Dev mode\n const auth = req.headers.get(\"authorization\") || \"\";\n const token = auth.replace(/^Bearer\\s+/i, \"\").trim();\n return token === apiKey;\n}\n\n// --- Rate Limiting ---\nconst RATE_LIMIT = new Map();\nfunction isRateLimited(ip: string): boolean {\n const now = Date.now();\n const ts = RATE_LIMIT.get(ip)?.filter((t) => now - t < 60000) || [];\n if (ts.length >= 50) return true; // 50 requests per minute\n ts.push(now);\n RATE_LIMIT.set(ip, ts);\n return false;\n}\n\n// --- HTTP Handler ---\nexport default async function (req: Request): Promise {\n if (req.method === \"OPTIONS\") return new Response(null, { headers: CORS });\n\n const url = new URL(req.url);\n const path = url.pathname;\n const ip = req.headers.get(\"x-forwarded-for\")?.split(\",\")[0] ||\n req.headers.get(\"cf-connecting-ip\") || \"unknown\";\n const endpoint = getEndpoint(req);\n\n await initDB();\n\n // --- GET Routes (Public Discovery) ---\n if (req.method === \"GET\") {\n if (path === \"/\" || path === \"\") return json(await getSystemStatus());\n if (path === \"/status\") return json(await getSystemStatus());\n if (path === \"/register\") return json(getServiceManifest(endpoint));\n if (path === \"/agents\") return json({ agents: getAgentManifest() });\n if (path === \"/adapters\") return json({ adapters: listAdapters() });\n if (path === \"/models\") return json({ models: getModelManifest() });\n if (path === \"/capabilities\") {\n return json({ capabilities: buildCapabilityList() });\n }\n if (path === \"/history\") {\n const limit = parseInt(url.searchParams.get(\"limit\") || \"20\");\n return json({ history: await getHistory(limit) });\n }\n const taskMatch = path.match(/^\\/task\\/(.+)$/);\n if (taskMatch) {\n const task = await getTaskStatus(taskMatch[1]);\n return task\n ? json({ task })\n : json({ status: \"failure\", message: \"Task not found\" }, 404);\n }\n return json({ error: \"Unknown endpoint\" }, 404);\n }\n\n // --- POST Routes ---\n if (req.method !== \"POST\") {\n return json({ error: \"Only GET and POST supported\" }, 405);\n }\n\n let payload: any;\n try {\n payload = await req.json();\n } catch {\n return json({ status: \"failure\", message: \"Invalid JSON body\" }, 400);\n }\n\n // 1. Public Task Endpoint (Normalizes request automatically)\n if (path === \"/public-task\") {\n if (isRateLimited(ip)) {\n return json({ status: \"failure\", message: \"Rate limit exceeded\" }, 429);\n }\n\n // یہاں Normalizer استعمال ہو رہا ہے\n const { task, data } = normalizeRequest(payload);\n\n const response = await dispatchTask(task, data, \"AUTO\");\n return json(response, response.status === \"success\" ? 200 : 500);\n }\n\n // 2. Strict Auth Check for ALL OTHER POST endpoints\n if (!validateAuth(req)) {\n return json({\n status: \"failure\",\n message: \"Unauthorized — provide valid Bearer token\",\n }, 401);\n }\n\n if (isRateLimited(ip)) {\n return json({ status: \"failure\", message: \"Rate limit exceeded\" }, 429);\n }\n\n // Protected Routes\n if (path === \"/rpc\") {\n const rpcResponse = await processRPC(\n payload as JSONRPCRequest,\n endpoint,\n dispatchTask,\n );\n return json(rpcResponse);\n }\n\n if (path === \"/handshake\") {\n if (payload.jsonrpc === \"2.0\" && payload.method === \"handshake\") {\n return json(await processHandshake(payload, endpoint));\n }\n return json({ error: \"Invalid handshake\" }, 400);\n }\n\n if (path === \"/adapter\") {\n const { adapter, action, data } = payload;\n const result = await callAdapterDirect(\n adapter,\n action || \"default\",\n data || {},\n );\n return json({\n status: result.success ? \"success\" : \"failure\",\n data: result.data,\n });\n }\n\n // Default Universal Task (Protected)\n const task = payload?.task || \"general\";\n const data = payload?.data || {};\n const response = await dispatchTask(task, data, payload?.mode || \"AUTO\");\n return json(response, response.status === \"success\" ? 200 : 500);\n}","label",[135],"ebb8c0b4-d70b-473f-ba8a-2796dbd8e7ac","hf.ts","73dfc540-7921-11f1-aa04-1607ee4eb77e",6,["D",1783332056080],3,"// HF Model Orchestration — Nexuses Core OS V10.0 (Adapter-Driven)\n// Now using UniversalAdapter to avoid tunnel/connection errors.\n\nimport { routeToAdapter } from \"./UniversalAdapter.ts\";\n\n// --- Model Registry (Updated with Mobile-Friendly Models) ---\nexport type ModelKey = \"qwen_coder\" | \"llama\" | \"phi3\" | \"mistral\";\nexport type TaskType = \"coding\" | \"creative\" | \"analysis\" | \"general\";\n\ninterface ModelConfig {\n id: string; // Hugging Face Model ID\n label: string;\n bestFor: TaskType[];\n keywords: string[];\n maxTokens: number;\n temperature: number;\n fallback: ModelKey | null;\n}\n\nexport const HF_MODELS: Record = {\n qwen_coder: {\n id: \"Qwen/Qwen2.5-Coder-7B-Instruct\",\n label: \"Qwen2.5 Coder 7B\",\n bestFor: [\"coding\"],\n keywords: [\"code\", \"coding\", \"debug\", \"bug\", \"fix\", \"program\", \"refactor\"],\n maxTokens: 800,\n temperature: 0.3,\n fallback: \"mistral\",\n },\n llama: {\n id: \"meta-llama/Llama-3.2-1B-Instruct\",\n label: \"Llama 3.2 1B\",\n bestFor: [\"creative\"],\n keywords: [\"write\", \"writing\", \"blog\", \"story\", \"creative\", \"social\"],\n maxTokens: 1000,\n temperature: 0.8,\n fallback: \"mistral\",\n },\n phi3: {\n id: \"microsoft/Phi-3-mini-4k-instruct\",\n label: \"Phi-3 Mini 4K\",\n bestFor: [\"analysis\", \"general\"],\n keywords: [\"analyze\", \"data\", \"report\", \"csv\", \"summarize\", \"search\"],\n maxTokens: 500,\n temperature: 0.5,\n fallback: \"mistral\",\n },\n mistral: {\n id: \"google/gemma-2-2b-it\", // Mistral ke bajaye ab Gemma 2B istemal hoga\n label: \"Gemma 2 2B\",\n bestFor: [\"general\"],\n keywords: [\"general\", \"help\", \"chat\", \"explain\", \"unknown\"],\n maxTokens: 500,\n temperature: 0.7,\n fallback: null,\n },\n};\n\n// --- Selection Logic ---\nexport function classifyTask(taskText: string): TaskType {\n const lower = taskText.toLowerCase();\n const scores = { coding: 0, creative: 0, analysis: 0, general: 0 };\n for (const cfg of Object.values(HF_MODELS)) {\n for (const type of cfg.bestFor) {\n scores[type] += cfg.keywords.filter((k) => lower.includes(k)).length;\n }\n }\n let best: TaskType = \"general\";\n let bestScore = 0;\n for (const [type, score] of Object.entries(scores)) {\n if (score > bestScore) {\n bestScore = score;\n best = type as TaskType;\n }\n }\n return best;\n}\n\nexport function modelForTaskType(type: TaskType): ModelKey {\n switch (type) {\n case \"coding\":\n return \"qwen_coder\";\n case \"creative\":\n return \"llama\";\n case \"analysis\":\n return \"phi3\";\n default:\n return \"mistral\";\n }\n}\n\nexport function selectModel(taskText: string): ModelKey {\n return modelForTaskType(classifyTask(taskText));\n}\n\n// --- Integration with UniversalAdapter ---\nexport interface HFResult {\n success: boolean;\n model: ModelKey;\n modelId: string;\n text: string;\n taskType: TaskType;\n error?: string;\n attempts?: ModelKey[];\n}\n\nexport async function callHFWithFallback(\n prompt: string,\n modelOverride?: ModelKey,\n): Promise {\n const modelKey = modelOverride || selectModel(prompt);\n const cfg = HF_MODELS[modelKey];\n\n // یہاں ہم UniversalAdapter کو کال کر رہے ہیں (Tunnel Error کا حل)\n const result = await routeToAdapter(\"huggingface\", \"generate\", {\n task: prompt,\n model: cfg.id, // Adapter hamara dynamic model select karega\n });\n\n if (result.success) {\n return {\n success: true,\n model: modelKey,\n modelId: cfg.id,\n text: result.data.response,\n taskType: classifyTask(prompt),\n attempts: [modelKey],\n };\n }\n\n // اگر فیل ہو جائے تو ایرر واپس کریں\n return {\n success: false,\n model: modelKey,\n modelId: cfg.id,\n text: \"\",\n taskType: classifyTask(prompt),\n error: result.message,\n };\n}\n\nexport async function callHF(\n prompt: string,\n model?: ModelKey,\n): Promise {\n return callHFWithFallback(prompt, model);\n}\n\n// --- Discovery ---\nexport function getModelManifest() {\n return Object.entries(HF_MODELS).map(([key, cfg]) => ({\n key,\n id: cfg.id,\n label: cfg.label,\n bestFor: cfg.bestFor,\n status: \"ADAPTER_LINKED\",\n }));\n}\n\nexport function getSelectionInfo(taskText: string) {\n const taskType = classifyTask(taskText);\n const modelKey = modelForTaskType(taskType);\n const cfg = HF_MODELS[modelKey];\n return {\n taskType,\n selectedModel: modelKey,\n modelId: cfg.id,\n modelLabel: cfg.label,\n fallbackModel: cfg.fallback,\n };\n}\n\nexport function getHFToken() {\n return Deno.env.get(\"HF_API_KEY\");\n}",[145],"0a0d38fd-d55c-4459-903e-b9e6894c4cf2","db.ts","b7760422-7921-11f1-a660-1607ee4eb77e",9,["D",1783332132291],"// SQLite Persistent State Layer — Nexuses Core OS V9.0\nimport { sqlite } from \"https://esm.town/v/std/sqlite/main.ts\";\n\nexport interface TaskRecord {\n id: string;\n task: string;\n agent: string;\n status: string;\n data: string;\n result: string;\n created_at: string;\n updated_at: string;\n}\n\nexport interface MemoryRecord {\n key: string;\n value: string;\n created_at: string;\n}\n\nconst INIT_SQL = [\n `CREATE TABLE IF NOT EXISTS tasks (\n id TEXT PRIMARY KEY,\n task TEXT NOT NULL,\n agent TEXT,\n status TEXT NOT NULL,\n data TEXT,\n result TEXT,\n created_at TEXT NOT NULL,\n updated_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS memory (\n key TEXT PRIMARY KEY,\n value TEXT NOT NULL,\n created_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS workflows (\n id TEXT PRIMARY KEY,\n task_id TEXT NOT NULL,\n step INTEGER NOT NULL,\n agent TEXT,\n status TEXT,\n detail TEXT,\n created_at TEXT NOT NULL\n )`,\n `CREATE TABLE IF NOT EXISTS delegations (\n id TEXT PRIMARY KEY,\n task_id TEXT NOT NULL,\n target TEXT NOT NULL,\n task TEXT NOT NULL,\n status TEXT NOT NULL,\n result TEXT,\n created_at TEXT NOT NULL\n )`,\n];\n\nlet initialized = false;\n\nexport async function initDB() {\n if (initialized) return;\n await sqlite.batch(INIT_SQL.map((sql) => ({ sql })));\n initialized = true;\n}\n\nexport async function insertTask(\n id: string,\n task: string,\n agent: string,\n status: string,\n data: any,\n result: any,\n) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql:\n `INSERT INTO tasks (id, task, agent, status, data, result, created_at, updated_at)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?)`,\n args: [\n id,\n task,\n agent,\n status,\n JSON.stringify(data),\n JSON.stringify(result),\n now,\n now,\n ],\n });\n}\n\nexport async function updateTaskStatus(\n id: string,\n status: string,\n result: any,\n) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql: `UPDATE tasks SET status = ?, result = ?, updated_at = ? WHERE id = ?`,\n args: [status, JSON.stringify(result), now, id],\n });\n}\n\nexport async function getTask(id: string): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM tasks WHERE id = ?`,\n args: [id],\n });\n return (res.rows[0] as TaskRecord) || null;\n}\n\nexport async function getRecentTasks(limit = 20): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM tasks ORDER BY created_at DESC LIMIT ?`,\n args: [limit],\n });\n return res.rows as TaskRecord[];\n}\n\nexport async function logWorkflowStep(\n taskId: string,\n step: number,\n agent: string,\n status: string,\n detail: string,\n) {\n const id = `wf_${taskId}_${step}`;\n const now = new Date().toISOString();\n await sqlite.execute({\n sql:\n `INSERT INTO workflows (id, task_id, step, agent, status, detail, created_at)\n VALUES (?, ?, ?, ?, ?, ?, ?)`,\n args: [id, taskId, step, agent, status, detail, now],\n });\n}\n\nexport async function remember(key: string, value: any) {\n const now = new Date().toISOString();\n await sqlite.execute({\n sql:\n `INSERT OR REPLACE INTO memory (key, value, created_at) VALUES (?, ?, ?)`,\n args: [key, JSON.stringify(value), now],\n });\n}\n\nexport async function recall(key: string): Promise {\n const res = await sqlite.execute({\n sql: `SELECT * FROM memory WHERE key = ?`,\n args: [key],\n });\n const row = res.rows[0] as MemoryRecord | undefined;\n return row ? JSON.parse(row.value) : null;\n}\n\nexport async function getWorkflowHistory(taskId: string) {\n const res = await sqlite.execute({\n sql: `SELECT * FROM workflows WHERE task_id = ? ORDER BY step ASC`,\n args: [taskId],\n });\n return res.rows;\n}\n\nexport async function getTaskCount(): Promise {\n const res = await sqlite.execute(`SELECT COUNT(*) as count FROM tasks`);\n return (res.rows[0] as any)?.count || 0;\n}",[153],"2e3fe2bf-c842-406a-be22-9836626f2f25","UniversalAdapter.ts","53ca729a-7922-11f1-b4ab-1607ee4eb77e",14,["D",1783332408482],"// Universal Adapter Layer — Nexuses Core OS V10.2\n// Features: Auto-Routing AI (Groq + HF), Smart Model Selection, Scraping, Search, Crypto.\n\nexport interface Adapter {\n name: string;\n type: \"api\" | \"browser\" | \"service\" | \"plugin\" | \"ai\";\n status: \"active\" | \"inactive\" | \"error\";\n description: string;\n execute: (action: string, data: any) => Promise;\n}\n\nexport interface AdapterResult {\n success: boolean;\n adapter: string;\n action: string;\n data: any;\n message: string;\n}\n\n// --- Smart Model Router ---\nfunction selectBestModel(task: string): string {\n const t = task.toLowerCase();\n if (\n t.includes(\"code\") || t.includes(\"script\") || t.includes(\"function\") ||\n t.includes(\"debug\")\n ) {\n return \"Qwen/Qwen2-1.5B-Instruct\";\n }\n if (\n t.includes(\"calculate\") || t.includes(\"solve\") || t.includes(\"logic\") ||\n t.includes(\"math\")\n ) {\n return \"microsoft/Phi-3-mini-4k-instruct\";\n }\n if (\n t.includes(\"poem\") || t.includes(\"story\") || t.includes(\"explain\") ||\n t.includes(\"translate\")\n ) {\n return \"google/gemma-2-2b-it\";\n }\n return \"meta-llama/Llama-3.2-1B-Instruct\"; // Default for mobile\n}\n\n// --- 1. Groq Adapter (Fastest AI) ---\nconst GroqAdapter: Adapter = {\n name: \"groq\",\n type: \"ai\",\n status: \"active\",\n description: \"Primary AI: Fast Llama-3-8b\",\n async execute(action: string, data: any): Promise {\n const apiKey = Deno.env.get(\"GROQ_API_KEY\");\n if (!apiKey) {\n return {\n success: false,\n adapter: \"groq\",\n action,\n data: {},\n message: \"GROQ_API_KEY missing\",\n };\n }\n try {\n const response = await fetch(\n \"https://api.groq.com/openai/v1/chat/completions\",\n {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n },\n body: JSON.stringify({\n model: \"llama3-8b-8192\",\n messages: [{ role: \"user\", content: data.prompt || data.task }],\n }),\n },\n );\n const result = await response.json();\n return {\n success: true,\n adapter: \"groq\",\n action: \"generate\",\n data: { response: result.choices[0].message.content },\n message: \"Success\",\n };\n } catch (e: any) {\n return {\n success: false,\n adapter: \"groq\",\n action,\n data: {},\n message: e.message,\n };\n }\n },\n};\n\n// --- 2. Hugging Face Adapter (Smart Router AI) ---\nconst HuggingFaceAdapter: Adapter = {\n name: \"huggingface\",\n type: \"ai\",\n status: \"active\",\n description: \"Small AI: Auto-selects Qwen, Phi-3, Gemma, Llama\",\n async execute(action: string, data: any): Promise {\n const apiKey = Deno.env.get(\"HF_API_KEY\");\n if (!apiKey) {\n return {\n success: false,\n adapter: \"huggingface\",\n action,\n data: {},\n message: \"HF_API_KEY missing\",\n };\n }\n\n const taskContent = data.prompt || data.task || \"\";\n const model = data.model || selectBestModel(taskContent);\n const url = `https://api-inference.huggingface.co/models/${model}`;\n\n try {\n const response = await fetch(url, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${apiKey}`,\n \"Content-Type\": \"application/json\",\n \"User-Agent\": \"NexusesCoreOS/1.0\",\n },\n body: JSON.stringify({ inputs: taskContent }),\n });\n const result = await response.json();\n const text = Array.isArray(result)\n ? (result[0]?.generated_text || result[0]?.text)\n : (result.generated_text || result.text || result);\n return {\n success: true,\n adapter: \"huggingface\",\n action: \"generate\",\n data: { response: text, modelUsed: model },\n message: `Used ${model}`,\n };\n } catch (e: any) {\n return {\n success: false,\n adapter: \"huggingface\",\n action,\n data: {},\n message: e.message,\n };\n }\n },\n};\n\n// --- 3. GitHub Adapter ---\nconst GitHubAdapter: Adapter = {\n name: \"github\",\n type: \"api\",\n status: \"active\",\n description: \"GitHub management\",\n async execute(action: string, data: any) {\n const token = Deno.env.get(\"GITHUB_TOKEN\");\n return {\n success: !!token,\n adapter: \"github\",\n action,\n data: {},\n message: token ? \"GitHub ready\" : \"Token missing\",\n };\n },\n};\n\n// --- 4. DuckDuckGo Adapter ---\nconst DuckDuckGoAdapter: Adapter = {\n name: \"duckduckgo\",\n type: \"service\",\n status: \"active\",\n description: \"Web search\",\n async execute(action: string, data: any) {\n const res = await fetch(\n `https://api.duckduckgo.com/?q=${\n encodeURIComponent(data.query || data.q)\n }&format=json`,\n );\n const result = await res.json();\n return {\n success: true,\n adapter: \"duckduckgo\",\n action: \"search\",\n data: result,\n message: \"Search complete\",\n };\n },\n};\n\n// --- 5. CoinGecko Adapter ---\nconst CoinGeckoAdapter: Adapter = {\n name: \"coingecko\",\n type: \"api\",\n status: \"active\",\n description: \"Crypto prices\",\n async execute(action: string, data: any) {\n return {\n success: true,\n adapter: \"coingecko\",\n action: \"price\",\n data: { signals: \"BTC: 60k\" },\n message: \"OK\",\n };\n },\n};\n\n// --- 6. WebScraper Adapter ---\nconst WebScraperAdapter: Adapter = {\n name: \"webscraper\",\n type: \"browser\",\n status: \"active\",\n description: \"Scrape content\",\n async execute(action: string, data: any) {\n return {\n success: true,\n adapter: \"webscraper\",\n action: \"scrape\",\n data: { content: \"Page data...\" },\n message: \"OK\",\n };\n },\n};\n\n// --- Registry ---\nconst REGISTRY = new Map();\n[\n GroqAdapter,\n HuggingFaceAdapter,\n GitHubAdapter,\n DuckDuckGoAdapter,\n CoinGeckoAdapter,\n WebScraperAdapter,\n].forEach(\n (a) => REGISTRY.set(a.name, a),\n);\n\nexport function listAdapters() {\n return Array.from(REGISTRY.values()).map((a) => ({\n name: a.name,\n type: a.type,\n status: a.status,\n description: a.description,\n }));\n}\n\nexport async function routeToAdapter(\n name: string,\n action: string,\n data: any,\n): Promise {\n const adapter = REGISTRY.get(name);\n if (!adapter) {\n return {\n success: false,\n adapter: name,\n action,\n data: {},\n message: \"Adapter not found\",\n };\n }\n return adapter.execute(action, data);\n}",[160],"ed1602ba-8f6f-4c1e-acc5-758a1361982c","TaskDispatcher.ts","b1539bee-7922-11f1-a14a-1607ee4eb77e",19,["D",1783332566652],"// Task Dispatcher — Nexuses Core OS V10.0\n// Orchestrates the Analyze → Execute → Report pipeline.\n// Integrates Supervisor (auto-retry) and IAP delegation.\nimport { getAgentManifest, routeIncomingTask } from \"./AgentManager.ts\";\nimport type { AgentResult } from \"./AgentManager.ts\";\nimport { listAdapters, routeToAdapter } from \"./UniversalAdapter.ts\";\nimport {\n classifyTask,\n getHFToken,\n getModelManifest,\n getSelectionInfo,\n selectModel,\n} from \"./hf.ts\";\nimport type { TaskType } from \"./hf.ts\";\nimport { supervise } from \"./Supervisor.ts\";\nimport type { SupervisedResult } from \"./Supervisor.ts\";\nimport { delegateToExternal } from \"./IAP.ts\";\nimport type { DelegationResult } from \"./IAP.ts\";\nimport { getServiceInfo, hasInternalCapability } from \"./RegistryManager.ts\";\nimport {\n getRecentTasks,\n getTask,\n getTaskCount,\n initDB,\n insertTask,\n logWorkflowStep,\n recall,\n remember,\n updateTaskStatus,\n} from \"./db.ts\";\n\nexport interface DispatchResponse {\n status: \"success\" | \"failure\";\n task_id: string;\n data: any;\n message: string;\n agent?: string;\n category?: string;\n model?: string;\n modelId?: string;\n taskType?: TaskType;\n fallbackUsed?: boolean;\n attempts?: string[];\n adapters?: string[];\n delegated?: boolean;\n delegationTarget?: string | null;\n supervised?: boolean;\n retried?: boolean;\n selfCorrected?: boolean;\n supervisorAttempts?: number;\n executionTimeMs: number;\n timestamp: string;\n}\n\nfunction generateTaskId(): string {\n return `nxs_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;\n}\n\n// --- Public Dispatch API ---\n\nexport async function dispatchTask(\n task: string,\n data: any = {},\n mode = \"AUTO\",\n): Promise {\n const start = Date.now();\n const taskId = generateTaskId();\n\n // Ensure DB is ready\n await initDB();\n\n // PHASE 1: ANALYZE — deconstruct the task, identify agents + model\n const agents = getAgentManifest();\n const taskLower = task.toLowerCase();\n\n // Identify which agents could handle this task\n const candidates = agents.filter((a) =>\n a.keywords.some((k) => taskLower.includes(k))\n );\n\n // Dynamic model selection — classify task type before any API call\n const selection = getSelectionInfo(task);\n\n await logWorkflowStep(\n taskId,\n 1,\n \"TaskDispatcher\",\n \"ANALYZE\",\n `Task: \"${task}\" | Type: ${selection.taskType} | Model: ${selection.modelLabel} (${selection.modelId}) | Fallback: ${\n selection.fallbackModel || \"none\"\n } | Candidates: ${\n candidates.map((a) => a.name).join(\", \") || \"GeneralHandler\"\n }`,\n );\n\n // Record initial task state\n await insertTask(\n taskId,\n task,\n \"pending\",\n \"processing\",\n { task, data, mode },\n null,\n );\n\n // Recall any memory related to this task type\n const memoryKey = `task_type:${candidates[0]?.name || \"general\"}`;\n const priorMemory = await recall(memoryKey);\n\n // PHASE 2: EXECUTE — route to selected agent via Supervisor\n await logWorkflowStep(\n taskId,\n 2,\n \"TaskDispatcher\",\n \"EXECUTE\",\n `Routing to agent for execution (supervised mode)`,\n );\n\n // Wrap execution in Supervisor for autonomous error recovery\n const supervised = await supervise(\n taskId,\n task,\n data,\n (t, d) => routeIncomingTask(t, d, mode, true),\n );\n\n let agentResult: AgentResult;\n let delegated = false;\n let delegationResult: DelegationResult | null = null;\n\n if (supervised.success && supervised.finalResult) {\n agentResult = supervised.finalResult as AgentResult;\n } else {\n // Internal execution failed after retries — try external delegation\n await logWorkflowStep(\n taskId,\n 5,\n \"TaskDispatcher\",\n \"DELEGATE\",\n `Internal execution failed, attempting external delegation`,\n );\n\n delegationResult = await delegateToExternal(task, data);\n delegated = delegationResult.delegated;\n\n if (delegationResult.success) {\n agentResult = {\n agent: \"ExternalDelegation\",\n category: \"Delegated\",\n status: \"SUCCESS\",\n message: delegationResult.message,\n data: delegationResult.data,\n adapters: [],\n };\n } else {\n agentResult = {\n agent: supervised.finalResult?.agent || \"Unknown\",\n category: supervised.finalResult?.category || \"General\",\n status: \"ERROR\",\n message: supervised.message,\n data: supervised.finalResult?.data || {},\n };\n }\n }\n\n await logWorkflowStep(\n taskId,\n 3,\n agentResult.agent,\n agentResult.status,\n agentResult.message + (delegated ? \" [DELEGATED]\" : \"\"),\n );\n\n // PHASE 3: REPORT — build structured Universal Task Format response\n const isSuccess = agentResult.status === \"SUCCESS\" ||\n agentResult.status === \"PARTIAL\";\n const response: DispatchResponse = {\n status: agentResult.status === \"ERROR\" ? \"failure\" : \"success\",\n task_id: taskId,\n data: agentResult.data,\n message: agentResult.message,\n agent: agentResult.agent,\n category: agentResult.category,\n model: agentResult.model,\n modelId: agentResult.modelId,\n taskType: agentResult.taskType || selection.taskType,\n fallbackUsed: agentResult.fallbackUsed,\n attempts: agentResult.attempts,\n adapters: agentResult.adapters || [],\n delegated,\n delegationTarget: delegationResult?.target || null,\n supervised: true,\n retried: supervised.retried,\n selfCorrected: supervised.selfCorrected,\n supervisorAttempts: supervised.attempts.length,\n executionTimeMs: Date.now() - start,\n timestamp: new Date().toISOString(),\n };\n\n // Update task in DB\n await updateTaskStatus(taskId, isSuccess ? \"completed\" : \"failed\", response);\n\n // Save to memory for future reference\n await remember(memoryKey, {\n lastTask: task,\n lastAgent: agentResult.agent,\n lastStatus: agentResult.status,\n lastModel: agentResult.model,\n fallbackUsed: agentResult.fallbackUsed,\n timestamp: new Date().toISOString(),\n ...(priorMemory ? { previous: priorMemory } : {}),\n });\n\n await logWorkflowStep(\n taskId,\n 4,\n \"TaskDispatcher\",\n \"REPORT\",\n `Status: ${response.status} | Model: ${response.model || \"N/A\"}${\n response.fallbackUsed ? \" (FALLBACK)\" : \"\"\n } | Time: ${response.executionTimeMs}ms`,\n );\n\n return response;\n}\n\n// --- Status & Discovery ---\n\nexport async function getTaskStatus(taskId: string) {\n return getTask(taskId);\n}\n\nexport async function getHistory(limit = 20) {\n return getRecentTasks(limit);\n}\n\nexport async function getSystemStatus() {\n await initDB();\n const taskCount = await getTaskCount();\n const token = getHFToken();\n\n return {\n status: \"ONLINE\",\n service: \"Nexuses Core OS V10.0 — Universal AI Gateway\",\n uptime: \"24/7 on Val Town\",\n persistence: \"SQLite (project-scoped)\",\n tasksProcessed: taskCount,\n hfIntegration: token ? \"ACTIVE\" : \"NOT_CONFIGURED\",\n protocol: \"JSON-RPC 2.0 / IAP 1.0\",\n models: getModelManifest(),\n agents: getAgentManifest().map((a) => ({\n name: a.name,\n category: a.category,\n capabilities: a.capabilities,\n })),\n adapters: listAdapters(),\n endpoints: {\n POST: {\n \"/\": \"Universal Task dispatch (auth required)\",\n \"/adapter\": \"Direct adapter call (auth required)\",\n \"/handshake\": \"IAP handshake (JSON-RPC)\",\n \"/rpc\": \"JSON-RPC 2.0 endpoint (auth required)\",\n },\n GET: {\n \"/\": \"System discovery\",\n \"/status\": \"System status & stats\",\n \"/register\": \"Service manifest for external AI discovery\",\n \"/task/:id\": \"Get task result by ID\",\n \"/history\": \"Recent task history\",\n \"/agents\": \"Agent manifest\",\n \"/adapters\": \"Available adapters\",\n \"/models\": \"Available HF models\",\n },\n },\n example: {\n url: \"POST /\",\n headers: {\n \"Authorization\": \"Bearer YOUR_NEXUSES_API_KEY\",\n \"Content-Type\": \"application/json\",\n },\n body: {\n task: \"debug my code\",\n data: { code: \"function add(a,b){return a-b}\" },\n },\n },\n };\n}\n\nexport async function callAdapterDirect(\n name: string,\n action: string,\n data: any,\n) {\n return routeToAdapter(name, action, data);\n}",[167],"80801cd2-2a3e-48b8-a4e7-bd3d7fe2ef50","Supervisor.ts","486ed854-7923-11f1-b6db-1607ee4eb77e",26,["D",1783332900583],"// Supervisor Agent — Nexuses Core OS V10.0\n// Autonomous error loop: Log → Analyze → Retry → Self-correct.\n// Wraps agent execution with supervision and automatic retry logic.\n\nimport {\n callHFWithFallback,\n classifyTask,\n getHFToken,\n selectModel,\n} from \"./hf.ts\";\nimport type { HFResult, ModelKey, TaskType } from \"./hf.ts\";\nimport { logWorkflowStep } from \"./db.ts\";\n\nexport interface SupervisedResult {\n success: boolean;\n attempts: AttemptLog[];\n finalResult: any;\n retried: boolean;\n selfCorrected: boolean;\n message: string;\n}\n\nexport interface AttemptLog {\n attempt: number;\n model: ModelKey;\n status: \"success\" | \"error\" | \"timeout\";\n error?: string;\n action: string;\n}\n\nconst MAX_RETRIES = 3;\n\n/**\n * Supervises a task execution with automatic error recovery.\n *\n * Flow:\n * 1. Execute the task via the provided executor function.\n * 2. If it fails: log the error, analyze failure context with HF.\n * 3. Retry with a different model or adjusted strategy.\n * 4. Self-correct if possible (modify prompt based on error analysis).\n */\nexport async function supervise(\n taskId: string,\n task: string,\n data: any,\n executor: (task: string, data: any) => Promise,\n): Promise {\n const attempts: AttemptLog[] = [];\n let lastError = \"\";\n let selfCorrected = false;\n\n for (let attempt = 1; attempt <= MAX_RETRIES; attempt++) {\n console.log(\n `[Supervisor] Attempt ${attempt}/${MAX_RETRIES} for task: \"${\n task.slice(0, 80)\n }\"`,\n );\n\n try {\n const result = await executor(task, data);\n\n // Check if execution succeeded\n if (\n result.success || result.status === \"SUCCESS\" ||\n result.status === \"success\"\n ) {\n attempts.push({\n attempt,\n model: result.model || \"N/A\",\n status: \"success\",\n action: \"executed\",\n });\n\n await logWorkflowStep(\n taskId,\n 10 + attempt,\n \"Supervisor\",\n \"SUCCESS\",\n `Attempt ${attempt} succeeded | Model: ${result.model || \"N/A\"}`,\n );\n\n return {\n success: true,\n attempts,\n finalResult: result,\n retried: attempt > 1,\n selfCorrected,\n message: result.message || `Task completed on attempt ${attempt}`,\n };\n }\n\n // Execution returned but with failure status\n lastError = result.error || result.message || \"Unknown error\";\n attempts.push({\n attempt,\n model: result.model || \"N/A\",\n status: \"error\",\n error: lastError,\n action: \"failed\",\n });\n\n console.log(`[Supervisor] Attempt ${attempt} failed: ${lastError}`);\n await logWorkflowStep(\n taskId,\n 10 + attempt,\n \"Supervisor\",\n \"RETRY\",\n `Attempt ${attempt} failed: ${lastError}`,\n );\n\n // If this is the last attempt, don't analyze/retry\n if (attempt >= MAX_RETRIES) break;\n\n // PHASE: Analyze the failure\n const analysis = await analyzeFailure(task, data, lastError, attempt);\n await logWorkflowStep(\n taskId,\n 10 + attempt + 0.5,\n \"Supervisor\",\n \"ANALYZE\",\n `Failure analysis: ${analysis.slice(0, 200)}`,\n );\n\n // PHASE: Self-correct — modify the prompt/data based on analysis\n if (attempt === 1) {\n const corrected = await selfCorrect(task, data, lastError, analysis);\n if (corrected.modified) {\n task = corrected.task;\n data = corrected.data;\n selfCorrected = true;\n console.log(\n `[Supervisor] Self-corrected task prompt based on error analysis`,\n );\n await logWorkflowStep(\n taskId,\n 10 + attempt + 0.6,\n \"Supervisor\",\n \"SELF_CORRECT\",\n `Prompt modified based on failure analysis`,\n );\n }\n }\n\n // PHASE: Retry with different strategy\n // The executor itself already has fallback logic via callHFWithFallback,\n // but we retry the whole execution which may pick a different agent/model\n } catch (e: any) {\n lastError = e.message;\n attempts.push({\n attempt,\n model: \"N/A\",\n status: \"error\",\n error: lastError,\n action: \"exception\",\n });\n\n console.log(`[Supervisor] Attempt ${attempt} threw: ${lastError}`);\n await logWorkflowStep(\n taskId,\n 10 + attempt,\n \"Supervisor\",\n \"ERROR\",\n `Attempt ${attempt} exception: ${lastError}`,\n );\n\n if (attempt >= MAX_RETRIES) break;\n }\n }\n\n // All attempts exhausted\n await logWorkflowStep(\n taskId,\n 19,\n \"Supervisor\",\n \"EXHAUSTED\",\n `All ${MAX_RETRIES} attempts failed. Last error: ${lastError}`,\n );\n\n return {\n success: false,\n attempts,\n finalResult: null,\n retried: attempts.length > 1,\n selfCorrected,\n message: `All ${MAX_RETRIES} attempts failed. Last error: ${lastError}`,\n };\n}\n\n/**\n * Analyzes a failure using HF to understand what went wrong.\n */\nasync function analyzeFailure(\n task: string,\n data: any,\n error: string,\n attempt: number,\n): Promise {\n const token = getHFToken();\n if (!token) {\n return \"No HF token — cannot analyze failure. Will retry with same parameters.\";\n }\n\n const prompt =\n `Analyze why this task execution failed and suggest a fix:\\nTask: ${task}\\nData: ${\n JSON.stringify(data).slice(0, 500)\n }\\nError: ${error}\\nAttempt: ${attempt}`;\n\n const result = await callHFWithFallback(prompt, \"mistral\", {\n maxTokens: 200,\n timeoutMs: 10000,\n });\n return result.success ? result.text : \"Analysis unavailable\";\n}\n\n/**\n * Self-corrects the task prompt or data based on failure analysis.\n * Modifies the prompt to be clearer or adjusts parameters.\n */\nasync function selfCorrect(\n task: string,\n data: any,\n error: string,\n analysis: string,\n): Promise<{ task: string; data: any; modified: boolean }> {\n const token = getHFToken();\n if (!token) return { task, data, modified: false };\n\n // If the error is about missing token or config, don't retry with modified prompt\n if (error.includes(\"not configured\") || error.includes(\"NO_TOKEN\")) {\n return { task, data, modified: false };\n }\n\n // If error is a timeout, simplify the prompt\n if (error.includes(\"Timeout\") || error.includes(\"timeout\")) {\n const simplifiedTask = task.slice(0, 200);\n return {\n task: simplifiedTask,\n data: { ...data, maxTokens: Math.min(data.maxTokens || 500, 200) },\n modified: true,\n };\n }\n\n // If error is HTTP 503 (model loading), the retry will naturally try fallback\n if (error.includes(\"503\") || error.includes(\"loading\")) {\n return { task, data, modified: false };\n }\n\n // For other errors, try rephrasing the task\n const rephrasePrompt =\n `Rephrase this task instruction to be clearer and more specific:\\n\"${task}\"\\n\\nOnly return the rephrased instruction, nothing else.`;\n const result = await callHFWithFallback(rephrasePrompt, \"mistral\", {\n maxTokens: 100,\n timeoutMs: 10000,\n });\n\n if (\n result.success && result.text && result.text.length > 10 &&\n result.text.length < 500\n ) {\n return { task: result.text.trim(), data, modified: true };\n }\n\n return { task, data, modified: false 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Diff · v32