🏥 Nelson-GPT

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\n \n

\n \n \n `, {\n headers: { 'Content-Type': 'text/html' }\n });\n }\n}\n\n// Handle other dynamic requests\nasync function handleDynamicRequest(request) {\n try {\n const networkResponse = await fetch(request);\n \n // Cache successful responses\n if (networkResponse.ok) {\n const cache = await caches.open(DYNAMIC_CACHE);\n cache.put(request, networkResponse.clone());\n }\n \n return networkResponse;\n } catch (error) {\n console.error('Dynamic request failed:', error);\n \n // Try to return cached response\n const cachedResponse = await caches.match(request);\n if (cachedResponse) {\n return cachedResponse;\n }\n \n throw error;\n }\n}\n\n// Handle background sync for offline messages\nself.addEventListener('sync', (event) => {\n if (event.tag === 'background-sync-messages') {\n event.waitUntil(syncOfflineMessages());\n }\n});\n\n// Sync offline messages when connection is restored\nasync function syncOfflineMessages() {\n try {\n // This would integrate with IndexedDB to sync offline messages\n console.log('Syncing offline messages...');\n \n // Implementation would go here to:\n // 1. Get offline messages from IndexedDB\n // 2. Send them to the server\n // 3. Update their sync status\n \n } catch (error) {\n console.error('Error syncing offline messages:', error);\n }\n}\n\n// Handle push notifications\nself.addEventListener('push', (event) => {\n const options = {\n body: event.data ? event.data.text() : 'New message from Nelson-GPT',\n icon: '/frontend/icon-192.png',\n badge: '/frontend/icon-72.png',\n vibrate: [100, 50, 100],\n data: {\n dateOfArrival: Date.now(),\n primaryKey: 1\n },\n actions: [\n {\n action: 'explore',\n title: 'Open App',\n icon: '/frontend/icon-192.png'\n },\n {\n action: 'close',\n title: 'Close',\n icon: '/frontend/icon-192.png'\n }\n ]\n };\n \n event.waitUntil(\n self.registration.showNotification('Nelson-GPT', options)\n );\n});\n\n// Handle notification clicks\nself.addEventListener('notificationclick', (event) => {\n event.notification.close();\n \n if (event.action === 'explore') {\n event.waitUntil(\n clients.openWindow('/')\n );\n }\n});\n\nconsole.log('Nelson-GPT Service Worker loaded');",[],[215,207],"frontend","aedfcb64-40dc-11f0-b28a-76b3cceeab13","manifest.json","aedfcbfa-40dc-11f0-b28a-76b3cceeab13",10,["D",1748997701932],"{\n \"name\": \"Nelson-GPT Medical AI Assistant\",\n \"short_name\": \"Nelson-GPT\",\n \"description\": \"AI-powered medical assistant for healthcare professionals and medical students, powered by Nelson Textbook of Pediatrics\",\n \"start_url\": \"/\",\n \"display\": \"standalone\",\n \"background_color\": \"#ffffff\",\n \"theme_color\": \"#1e40af\",\n \"orientation\": \"portrait-primary\",\n \"scope\": \"/\",\n \"lang\": \"en\",\n \"categories\": [\"medical\", \"education\", \"productivity\"],\n \"icons\": [\n {\n \"src\": \"/frontend/icon-72.png\",\n \"sizes\": \"72x72\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n },\n {\n \"src\": \"/frontend/icon-96.png\",\n \"sizes\": \"96x96\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n },\n {\n \"src\": \"/frontend/icon-128.png\",\n \"sizes\": \"128x128\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n },\n {\n \"src\": \"/frontend/icon-144.png\",\n \"sizes\": \"144x144\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n },\n {\n \"src\": \"/frontend/icon-152.png\",\n \"sizes\": \"152x152\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n },\n {\n \"src\": \"/frontend/icon-192.png\",\n \"sizes\": \"192x192\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n },\n {\n \"src\": \"/frontend/icon-384.png\",\n \"sizes\": \"384x384\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n },\n {\n \"src\": \"/frontend/icon-512.png\",\n \"sizes\": \"512x512\",\n \"type\": \"image/png\",\n \"purpose\": \"any maskable\"\n }\n ],\n \"shortcuts\": [\n {\n \"name\": \"New Chat\",\n \"short_name\": \"Chat\",\n \"description\": \"Start a new medical consultation\",\n \"url\": \"/chat\",\n \"icons\": [\n {\n \"src\": \"/frontend/icon-192.png\",\n \"sizes\": \"192x192\"\n }\n ]\n }\n ],\n \"screenshots\": [\n {\n \"src\": \"/frontend/screenshot-mobile.png\",\n \"sizes\": \"390x844\",\n \"type\": \"image/png\",\n \"form_factor\": \"narrow\"\n },\n {\n \"src\": \"/frontend/screenshot-desktop.png\",\n \"sizes\": \"1280x720\",\n \"type\": \"image/png\",\n \"form_factor\": \"wide\"\n }\n ],\n \"features\": [\n \"Cross Platform\",\n \"fast\",\n \"simple\"\n ],\n \"edge_side_panel\": {\n \"preferred_width\": 400\n },\n \"launch_handler\": {\n \"client_mode\": \"navigate-existing\"\n }\n}",[],[215,217],"c55967c4-40dc-11f0-9448-76b3cceeab13","index.tsx","c5596882-40dc-11f0-9448-76b3cceeab13",12,["D",1748997739638],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from 'https://esm.sh/react@18.2.0';\nimport { createRoot } from 'https://esm.sh/react-dom@18.2.0/client';\nimport App from './components/App.tsx';\n\n// Initialize the React app\nconst root = createRoot(document.getElementById('root')!);\nroot.render();",[],[215,225],"a788d16c-40dc-11f0-85cc-76b3cceeab13","index.html","a788d234-40dc-11f0-85cc-76b3cceeab13",9,["D",1748997689616],"\n\n\n \n \n Nelson-GPT - Medical AI Assistant\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n

\n \n \n \n \n \n \n\n",[],[215,233],"3b9f6460-40dd-11f0-9d77-76b3cceeab13","api.ts","3b9f6514-40dd-11f0-9d77-76b3cceeab13","705ceaed-5d28-4b7f-aedd-0d7e3dcac0f9",["D",1748997938065],"// API client for Nelson-GPT\nimport type { \n ChatRequest, \n ChatResponse, \n EmbeddingRequest, \n EmbeddingResponse,\n ApiResponse \n} from '../../shared/types.ts';\n\nclass ApiClient {\n private baseUrl: string;\n\n constructor(baseUrl: string = '/api') {\n this.baseUrl = baseUrl;\n }\n\n private async request(\n endpoint: string, \n options: RequestInit = {}\n ): Promise> {\n try {\n const url = `${this.baseUrl}${endpoint}`;\n const response = await fetch(url, {\n headers: {\n 'Content-Type': 'application/json',\n ...options.headers,\n },\n ...options,\n });\n\n if (!response.ok) {\n const errorData = await response.json().catch(() => ({}));\n throw new Error(errorData.error || `HTTP error! status: ${response.status}`);\n }\n\n const data = await response.json();\n return {\n success: true,\n data\n };\n } catch (error: any) {\n console.error(`API request failed for ${endpoint}:`, error);\n return {\n success: false,\n error: error.message,\n message: 'Request failed'\n };\n }\n }\n\n // Chat endpoints\n async sendChatMessage(request: ChatRequest): Promise> {\n return this.request('/chat', {\n method: 'POST',\n body: JSON.stringify(request),\n });\n }\n\n async streamChatMessage(request: ChatRequest): Promise {\n const url = `${this.baseUrl}/chat/stream`;\n const response = await fetch(url, {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(request),\n });\n\n if (!response.ok) {\n throw new Error(`HTTP error! status: ${response.status}`);\n }\n\n return response;\n }\n\n async getChatHistory(sessionId: string): Promise> {\n return this.request(`/chat/history/${sessionId}`);\n }\n\n // Embedding endpoints\n async generateEmbedding(request: EmbeddingRequest): Promise> {\n return this.request('/embeddings/generate', {\n method: 'POST',\n body: JSON.stringify(request),\n });\n }\n\n async searchSimilarContent(query: {\n text?: string;\n embedding?: number[];\n limit?: number;\n threshold?: number;\n }): Promise> {\n return this.request('/embeddings/search', {\n method: 'POST',\n body: JSON.stringify(query),\n });\n }\n\n // Health check endpoints\n async healthCheck(): Promise> {\n return this.request('/health');\n }\n\n async chatHealthCheck(): Promise> {\n return this.request('/chat/health');\n }\n\n async embeddingsHealthCheck(): Promise> {\n return this.request('/embeddings/health');\n }\n\n // Utility methods\n isOnline(): boolean {\n return navigator.onLine;\n }\n\n async checkConnectivity(): Promise {\n try {\n const result = await this.healthCheck();\n return result.success;\n } catch {\n return false;\n }\n }\n}\n\n// Create singleton instance\nexport const apiClient = new ApiClient();\n\n// Export for use in components\nexport default apiClient;",[],[215,248,241],"services","3b9f7086-40dd-11f0-9d77-76b3cceeab13",["D",1748997938065],[184],[215,248],"3165c002-40dd-11f0-91bb-76b3cceeab13","useChat.ts","3165c0d4-40dd-11f0-91bb-76b3cceeab13","62aad775-df7a-4232-bf38-a6532d663da9",18,["D",1748997920911],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useCallback, useRef } from 'https://esm.sh/react@18.2.0';\nimport type { Message, VectorSearchResult, StreamingChatResponse } from '../../shared/types.ts';\n\ninterface ChatState {\n messages: Message[];\n isLoading: boolean;\n error: string | null;\n sessionId: string | null;\n lastSources: VectorSearchResult[];\n}\n\ninterface ChatActions {\n sendMessage: (content: string) => Promise;\n clearMessages: () => void;\n retryLastMessage: () => Promise;\n}\n\nexport function useChat(): ChatState & ChatActions {\n const [messages, setMessages] = useState([]);\n const [isLoading, setIsLoading] = useState(false);\n const [error, setError] = useState(null);\n const [sessionId, setSessionId] = useState(null);\n const [lastSources, setLastSources] = useState([]);\n \n const abortControllerRef = useRef(null);\n const lastUserMessageRef = useRef('');\n\n const sendMessage = useCallback(async (content: string) => {\n if (isLoading) return;\n\n lastUserMessageRef.current = content;\n setIsLoading(true);\n setError(null);\n\n // Add user message\n const userMessage: Message = {\n id: crypto.randomUUID(),\n content,\n role: 'user',\n timestamp: new Date()\n };\n\n setMessages(prev => [...prev, userMessage]);\n\n // Create assistant message placeholder\n const assistantMessageId = crypto.randomUUID();\n const assistantMessage: Message = {\n id: assistantMessageId,\n content: '',\n role: 'assistant',\n timestamp: new Date(),\n isStreaming: true\n };\n\n setMessages(prev => [...prev, assistantMessage]);\n\n try {\n // Cancel any ongoing request\n if (abortControllerRef.current) {\n abortControllerRef.current.abort();\n }\n\n abortControllerRef.current = new AbortController();\n\n // Check if streaming is supported and enabled\n const config = (window as any).__NELSON_GPT_CONFIG__;\n const useStreaming = config?.features?.streaming !== false;\n\n if (useStreaming && typeof EventSource !== 'undefined') {\n await handleStreamingResponse(content, assistantMessageId);\n } else {\n await handleNonStreamingResponse(content, assistantMessageId);\n }\n } catch (err: any) {\n console.error('Chat error:', err);\n \n if (err.name === 'AbortError') {\n // Request was cancelled\n setMessages(prev => prev.filter(msg => msg.id !== assistantMessageId));\n } else {\n // Handle error\n const errorMessage = err.message || 'An error occurred while processing your request.';\n setError(errorMessage);\n \n setMessages(prev => prev.map(msg => \n msg.id === assistantMessageId \n ? { ...msg, content: `Error: ${errorMessage}`, isStreaming: false }\n : msg\n ));\n }\n } finally {\n setIsLoading(false);\n abortControllerRef.current = null;\n }\n }, [isLoading, sessionId]);\n\n const handleStreamingResponse = async (content: string, assistantMessageId: string) => {\n const response = await fetch('/api/chat/stream', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n message: content,\n sessionId\n }),\n signal: abortControllerRef.current?.signal\n });\n\n if (!response.ok) {\n throw new Error(`HTTP error! status: ${response.status}`);\n }\n\n const reader = response.body?.getReader();\n if (!reader) {\n throw new Error('No response body available');\n }\n\n const decoder = new TextDecoder();\n let buffer = '';\n let accumulatedContent = '';\n\n try {\n while (true) {\n const { done, value } = await reader.read();\n \n if (done) break;\n \n buffer += decoder.decode(value, { stream: true });\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n if (line.trim() === '') continue;\n \n if (line.startsWith('data: ')) {\n const data = line.slice(6);\n if (data === '[DONE]') continue;\n \n try {\n const chunk: StreamingChatResponse = JSON.parse(data);\n \n switch (chunk.type) {\n case 'start':\n if (chunk.sessionId) {\n setSessionId(chunk.sessionId);\n }\n if (chunk.sources) {\n setLastSources(chunk.sources);\n }\n break;\n \n case 'token':\n accumulatedContent += chunk.content;\n setMessages(prev => prev.map(msg => \n msg.id === assistantMessageId \n ? { ...msg, content: accumulatedContent }\n : msg\n ));\n break;\n \n case 'complete':\n setMessages(prev => prev.map(msg => \n msg.id === assistantMessageId \n ? { ...msg, isStreaming: false }\n : msg\n ));\n if (chunk.sources) {\n setLastSources(chunk.sources);\n }\n break;\n \n case 'error':\n throw new Error(chunk.content);\n }\n } catch (parseError) {\n console.error('Error parsing stream chunk:', parseError);\n }\n }\n }\n }\n } finally {\n reader.releaseLock();\n }\n };\n\n const handleNonStreamingResponse = async (content: string, assistantMessageId: string) => {\n const response = await fetch('/api/chat', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n message: content,\n sessionId\n }),\n signal: abortControllerRef.current?.signal\n });\n\n if (!response.ok) {\n const errorData = await response.json().catch(() => ({}));\n throw new Error(errorData.error || `HTTP error! status: ${response.status}`);\n }\n\n const data = await response.json();\n \n setMessages(prev => prev.map(msg => \n msg.id === assistantMessageId \n ? { \n ...msg, \n content: data.response || 'No response received',\n isStreaming: false \n }\n : msg\n ));\n\n if (data.sessionId) {\n setSessionId(data.sessionId);\n }\n\n if (data.sources) {\n setLastSources(data.sources);\n }\n };\n\n const clearMessages = useCallback(() => {\n setMessages([]);\n setSessionId(null);\n setLastSources([]);\n setError(null);\n \n // Cancel any ongoing request\n if (abortControllerRef.current) {\n abortControllerRef.current.abort();\n }\n }, []);\n\n const retryLastMessage = useCallback(async () => {\n if (lastUserMessageRef.current && !isLoading) {\n // Remove the last assistant message if it exists and has an error\n setMessages(prev => {\n const lastMessage = prev[prev.length - 1];\n if (lastMessage?.role === 'assistant' && (error || lastMessage.content.startsWith('Error:'))) {\n return prev.slice(0, -1);\n }\n return prev;\n });\n \n await sendMessage(lastUserMessageRef.current);\n }\n }, [sendMessage, isLoading, error]);\n\n return {\n messages,\n isLoading,\n error,\n sessionId,\n lastSources,\n sendMessage,\n clearMessages,\n retryLastMessage\n };\n}",[],[215,262,254],"hooks","31660508-40dd-11f0-91bb-76b3cceeab13",["D",1748997920911],[182],[215,262],"dd38c5ec-40dc-11f0-bf1a-76b3cceeab13","SplashScreen.tsx","dd38c6e6-40dc-11f0-bf1a-76b3cceeab13","2fa00c87-662b-4a41-a0da-a2956d4a8f7a",14,["D",1748997779689],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useEffect, useState } from 'https://esm.sh/react@18.2.0';\n\nconst SplashScreen: React.FC = () => {\n const [progress, setProgress] = useState(0);\n const [loadingText, setLoadingText] = useState('Initializing Nelson-GPT');\n\n useEffect(() => {\n const loadingSteps = [\n { text: 'Initializing Nelson-GPT', duration: 500 },\n { text: 'Loading medical knowledge base', duration: 800 },\n { text: 'Connecting to AI services', duration: 600 },\n { text: 'Preparing your medical assistant', duration: 600 }\n ];\n\n let currentStep = 0;\n let currentProgress = 0;\n\n const updateProgress = () => {\n if (currentStep < loadingSteps.length) {\n setLoadingText(loadingSteps[currentStep].text);\n \n const stepProgress = 100 / loadingSteps.length;\n const targetProgress = (currentStep + 1) * stepProgress;\n \n const progressInterval = setInterval(() => {\n currentProgress += 2;\n setProgress(Math.min(currentProgress, targetProgress));\n \n if (currentProgress >= targetProgress) {\n clearInterval(progressInterval);\n currentStep++;\n \n setTimeout(() => {\n updateProgress();\n }, 200);\n }\n }, 50);\n }\n };\n\n updateProgress();\n }, []);\n\n return (\n

\n {/* Medical-themed animated icon */}\n

\n \n \n \n

\n \n {/* Pulse animation */}\n

\n\n {/* App Title */}\n

\n Nelson-GPT\n

\n \n

\n Your AI-Powered Medical Assistant\n

\n\n {/* Loading Progress */}\n

\n {loadingText}\n

\n \n

\n {Math.round(progress)}%\n

\n\n {/* Medical badges */}\n

\n \n Evidence-Based\n

\n \n Pediatric Focus\n

\n \n AI-Powered\n

\n\n {/* Disclaimer */}\n

\n For educational purposes only. Always consult healthcare professionals for patient care.\n

\n \n );\n};\n\nexport default SplashScreen;",[],[215,276,268],"components","20157522-40dd-11f0-8712-76b3cceeab13","Sidebar.tsx","201575f4-40dd-11f0-8712-76b3cceeab13",17,["D",1748997891865],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useState } from 'https://esm.sh/react@18.2.0';\nimport type { AppSettings, UserProfile } from '../../shared/types.ts';\n\ninterface SidebarProps {\n isOpen: boolean;\n onClose: () => void;\n settings: AppSettings;\n onSettingsChange: (settings: Partial) => void;\n userProfile: UserProfile | null;\n onProfileChange: (profile: UserProfile) => void;\n isInstallable: boolean;\n onInstallPWA: () => void;\n}\n\nconst Sidebar: React.FC = ({\n isOpen,\n onClose,\n settings,\n onSettingsChange,\n userProfile,\n onProfileChange,\n isInstallable,\n onInstallPWA\n}) => {\n const [activeTab, setActiveTab] = useState<'profile' | 'settings' | 'help'>('profile');\n const [isEditingProfile, setIsEditingProfile] = useState(false);\n const [profileForm, setProfileForm] = useState>(\n userProfile || {\n name: '',\n email: '',\n role: 'medical_student',\n specialization: '',\n institution: ''\n }\n );\n\n const handleProfileSave = () => {\n if (profileForm.name && profileForm.email && profileForm.role) {\n const newProfile: UserProfile = {\n id: userProfile?.id || crypto.randomUUID(),\n name: profileForm.name,\n email: profileForm.email,\n role: profileForm.role as UserProfile['role'],\n specialization: profileForm.specialization,\n institution: profileForm.institution\n };\n onProfileChange(newProfile);\n setIsEditingProfile(false);\n }\n };\n\n const ProfileTab = () => (\n

\n 👤\n

\n \n {userProfile && !isEditingProfile ? (\n

{userProfile.name}

{userProfile.email}

\n {userProfile.role.replace('_', ' ')}\n

\n {userProfile.specialization && (\n

{userProfile.specialization}

\n )}\n {userProfile.institution && (\n

{userProfile.institution}

\n )}\n \n setIsEditingProfile(true)}\n className=\"mt-3 px-4 py-2 text-sm bg-blue-600 text-white rounded-lg hover:bg-blue-700\"\n >\n Edit Profile\n \n

\n ) : (\n

\n {userProfile ? 'Edit Profile' : 'Create Profile'}\n

\n \n

\n setProfileForm({ ...profileForm, name: e.target.value })}\n className=\"w-full px-3 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500\"\n />\n \n setProfileForm({ ...profileForm, email: e.target.value })}\n className=\"w-full px-3 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500\"\n />\n \n setProfileForm({ ...profileForm, role: e.target.value as UserProfile['role'] })}\n className=\"w-full px-3 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500\"\n >\n \n \n \n \n \n setProfileForm({ ...profileForm, specialization: e.target.value })}\n className=\"w-full px-3 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500\"\n />\n \n setProfileForm({ ...profileForm, institution: e.target.value })}\n className=\"w-full px-3 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500\"\n />\n

\n \n

\n \n Save\n \n {userProfile && (\n setIsEditingProfile(false)}\n className=\"px-4 py-2 bg-gray-300 text-gray-700 rounded-lg hover:bg-gray-400\"\n >\n Cancel\n \n )}\n

\n )}\n

\n\n {/* Quick Stats */}\n

Usage Statistics

\n Questions Asked:\n 0\n

\n Sessions:\n 0\n

\n Last Active:\n Today\n

\n );\n\n const SettingsTab = () => (\n

Settings

\n \n {/* Theme */}\n

\n Theme\n onSettingsChange({ theme: e.target.value as AppSettings['theme'] })}\n className=\"w-full px-3 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500\"\n >\n \n \n \n \n

\n\n {/* Font Size */}\n

\n Font Size\n onSettingsChange({ fontSize: e.target.value as AppSettings['fontSize'] })}\n className=\"w-full px-3 py-2 border border-gray-300 rounded-lg focus:outline-none focus:ring-2 focus:ring-blue-500\"\n >\n \n \n \n \n

\n\n {/* Notifications */}\n

\n Notifications\n onSettingsChange({ notifications: !settings.notifications })}\n className={`relative inline-flex h-6 w-11 items-center rounded-full transition-colors ${\n settings.notifications ? 'bg-blue-600' : 'bg-gray-200'\n }`}\n >\n \n \n

\n\n {/* Offline Mode */}\n

\n Offline Mode\n onSettingsChange({ offlineMode: !settings.offlineMode })}\n className={`relative inline-flex h-6 w-11 items-center rounded-full transition-colors ${\n settings.offlineMode ? 'bg-blue-600' : 'bg-gray-200'\n }`}\n >\n \n \n

\n\n {/* Streaming Responses */}\n

\n Streaming Responses\n onSettingsChange({ streamingResponses: !settings.streamingResponses })}\n className={`relative inline-flex h-6 w-11 items-center rounded-full transition-colors ${\n settings.streamingResponses ? 'bg-blue-600' : 'bg-gray-200'\n }`}\n >\n \n \n

\n\n {/* PWA Install */}\n {isInstallable && (\n

Install App

\n Install Nelson-GPT for a better experience with offline support and push notifications.\n

\n \n Install Nelson-GPT\n \n

\n )}\n

\n );\n\n const HelpTab = () => (\n

Help & Support

\n \n {/* About */}\n

About Nelson-GPT

\n Nelson-GPT is an AI-powered medical assistant specializing in pediatric medicine, \n powered by the Nelson Textbook of Pediatrics.\n

\n Version 1.0.0\n

\n\n {/* Features */}\n

Features

\n \n Evidence-based medical responses\n
\n \n Real-time streaming responses\n
\n \n Offline functionality\n
\n \n Source citations from Nelson Textbook\n
\n \n Progressive Web App (PWA)\n

\n\n {/* Disclaimer */}\n

⚠️ Medical Disclaimer

\n This application is for educational and informational purposes only. \n It should not be used as a substitute for professional medical advice, \n diagnosis, or treatment. Always seek the advice of qualified healthcare \n providers with any questions regarding medical conditions.\n

\n\n {/* Contact */}\n

Support

\n 📧\n support@nelson-gpt.com\n

\n 🌐\n www.nelson-gpt.com\n

\n );\n\n return (\n \n {/* Header */}\n

Nelson-GPT

\n \n \n \n

\n\n {/* Tab Navigation */}\n

\n {[\n { id: 'profile', label: 'Profile', icon: '👤' },\n { id: 'settings', label: 'Settings', icon: '⚙️' },\n { id: 'help', label: 'Help', icon: '❓' }\n ].map((tab) => (\n setActiveTab(tab.id as any)}\n className={`flex-1 px-4 py-3 text-sm font-medium border-b-2 transition-colors ${\n activeTab === tab.id\n ? 'border-blue-500 text-blue-600'\n : 'border-transparent text-gray-500 hover:text-gray-700'\n }`}\n >\n {tab.icon}\n {tab.label}\n \n ))}\n

\n\n {/* Tab Content */}\n

\n {activeTab === 'profile' && }\n {activeTab === 'settings' && }\n {activeTab === 'help' && }\n

\n \n );\n};\n\nexport default Sidebar;",[],[215,276,278],"e8fb5f8e-40dc-11f0-a3c3-76b3cceeab13","MessageBubble.tsx","e8fb6042-40dc-11f0-a3c3-76b3cceeab13",15,["D",1748997799419],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React from 'https://esm.sh/react@18.2.0';\nimport type { Message, VectorSearchResult } from '../../shared/types.ts';\n\ninterface MessageBubbleProps {\n message: Message;\n sources?: VectorSearchResult[];\n isTyping?: boolean;\n}\n\nconst MessageBubble: React.FC = ({ message, sources, isTyping }) => {\n const isUser = message.role === 'user';\n const isAssistant = message.role === 'assistant';\n\n const formatTime = (date: Date) => {\n return new Intl.DateTimeFormat('en-US', {\n hour: '2-digit',\n minute: '2-digit',\n hour12: true\n }).format(date);\n };\n\n return (\n

\n {/* Avatar */}\n

\n {isUser ? '👤' : '🏥'}\n

\n\n {/* Message Content */}\n

\n {/* Typing indicator */}\n {isTyping && isAssistant && (\n

\n Nelson-GPT is thinking...\n

\n )}\n\n {/* Message text */}\n {!isTyping && (\n

\n {message.content}\n

\n )}\n\n {/* Sources for assistant messages */}\n {!isTyping && isAssistant && sources && sources.length > 0 && (\n

\n 📚 Sources from Nelson Textbook:\n

\n {sources.slice(0, 3).map((source, index) => (\n

\n {source.metadata?.chapter && `Chapter: ${source.metadata.chapter}`}\n {source.metadata?.section && ` • Section: ${source.metadata.section}`}\n

\n {source.content.substring(0, 100)}...\n

\n Relevance: {Math.round((source.similarity || 0) * 100)}%\n

\n ))}\n

\n )}\n

\n\n {/* Timestamp */}\n

\n {formatTime(message.timestamp)}\n {message.isStreaming && (\n \n )}\n

\n );\n};\n\nexport default MessageBubble;",[],[215,276,286],"ff76fade-40dc-11f0-82f9-76b3cceeab13","ChatInterface.tsx","ff76fb74-40dc-11f0-82f9-76b3cceeab13",16,["D",1748997837139],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useState, useRef, useEffect } from 'https://esm.sh/react@18.2.0';\nimport MessageBubble from './MessageBubble.tsx';\nimport type { AppSettings } from '../../shared/types.ts';\n\ninterface ChatInterfaceProps {\n chat: any; // Chat hook\n settings: AppSettings;\n onMenuClick: () => void;\n}\n\nconst ChatInterface: React.FC = ({ chat, settings, onMenuClick }) => {\n const [inputValue, setInputValue] = useState('');\n const [isComposing, setIsComposing] = useState(false);\n const messagesEndRef = useRef(null);\n const inputRef = useRef(null);\n\n const scrollToBottom = () => {\n messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });\n };\n\n useEffect(() => {\n scrollToBottom();\n }, [chat.messages, chat.isLoading]);\n\n const handleSubmit = async (e: React.FormEvent) => {\n e.preventDefault();\n \n if (!inputValue.trim() || chat.isLoading) return;\n\n const message = inputValue.trim();\n setInputValue('');\n \n // Auto-resize textarea\n if (inputRef.current) {\n inputRef.current.style.height = 'auto';\n }\n\n await chat.sendMessage(message);\n };\n\n const handleKeyDown = (e: React.KeyboardEvent) => {\n if (e.key === 'Enter' && !e.shiftKey && !isComposing) {\n e.preventDefault();\n handleSubmit(e);\n }\n };\n\n const handleInputChange = (e: React.ChangeEvent) => {\n setInputValue(e.target.value);\n \n // Auto-resize textarea\n const textarea = e.target;\n textarea.style.height = 'auto';\n textarea.style.height = Math.min(textarea.scrollHeight, 120) + 'px';\n };\n\n const suggestedQuestions = [\n \"What are the common symptoms of pediatric pneumonia?\",\n \"How do you diagnose and treat febrile seizures in children?\",\n \"What are the vaccination schedules for infants?\",\n \"Explain the management of pediatric asthma exacerbations\",\n \"What are the signs of dehydration in children?\"\n ];\n\n return (\n

\n {/* Header */}\n

\n \n \n \n \n

\n 🏥\n

Nelson-GPT

Medical AI Assistant

\n\n

\n {/* Connection status */}\n

\n \n

\n\n {/* Messages Area */}\n

\n {chat.messages.length === 0 ? (\n /* Welcome Screen */\n

\n 🏥\n

\n \n

\n Welcome to Nelson-GPT\n

\n \n

\n Your AI-powered medical assistant specializing in pediatric medicine. \n Ask questions about child health, diagnosis, and treatment based on the Nelson Textbook of Pediatrics.\n

\n\n {/* Suggested Questions */}\n

Try asking:

\n {suggestedQuestions.map((question, index) => (\n setInputValue(question)}\n className=\"text-left p-3 bg-white border border-gray-200 rounded-lg hover:border-blue-300 hover:bg-blue-50 transition-colors text-sm\"\n >\n {question}\n \n ))}\n

\n\n {/* Disclaimer */}\n

\n ⚠️\n

\n Medical Disclaimer: This AI assistant is for educational and informational purposes only. \n Always consult with qualified healthcare professionals for actual patient care and medical decisions.\n

\n ) : (\n /* Messages */\n

\n {chat.messages.map((message, index) => (\n \n ))}\n \n {/* Typing indicator */}\n {chat.isLoading && (\n \n )}\n \n

\n )}\n

\n\n {/* Input Area */}\n

\n setIsComposing(true)}\n onCompositionEnd={() => setIsComposing(false)}\n placeholder=\"Ask about pediatric medicine...\"\n className=\"w-full px-4 py-3 border border-gray-300 rounded-2xl focus:outline-none focus:ring-2 focus:ring-blue-500 focus:border-transparent resize-none max-h-32\"\n rows={1}\n disabled={chat.isLoading}\n />\n \n {/* Character count */}\n {inputValue.length > 0 && (\n

\n {inputValue.length}/1000\n

\n )}\n

\n\n \n {chat.isLoading ? (\n \n ) : (\n \n )}\n \n \n\n {/* Quick actions */}\n

\n Press Enter to send, Shift+Enter for new line\n

\n \n {chat.messages.length > 0 && (\n \n Clear chat\n \n )}\n

\n );\n};\n\nexport default ChatInterface;",[],[215,276,294],"d03b0242-40dc-11f0-b991-76b3cceeab13","App.tsx","d03b02f6-40dc-11f0-b991-76b3cceeab13",13,["D",1748997757889],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport React, { useState, useEffect } from 'https://esm.sh/react@18.2.0';\nimport SplashScreen from './SplashScreen.tsx';\nimport ChatInterface from './ChatInterface.tsx';\nimport Sidebar from './Sidebar.tsx';\nimport { useChat } from '../hooks/useChat.ts';\nimport type { AppSettings, UserProfile } from '../../shared/types.ts';\n\nconst App: React.FC = () => {\n const [isLoading, setIsLoading] = useState(true);\n const [isSidebarOpen, setIsSidebarOpen] = useState(false);\n const [settings, setSettings] = useState({\n theme: 'light',\n fontSize: 'medium',\n notifications: true,\n offlineMode: true,\n streamingResponses: true\n });\n const [userProfile, setUserProfile] = useState(null);\n const [isInstallable, setIsInstallable] = useState(false);\n\n const chat = useChat();\n\n useEffect(() => {\n // Simulate loading time for splash screen\n const timer = setTimeout(() => {\n setIsLoading(false);\n }, 2500);\n\n // Listen for PWA install prompt\n const handleInstallable = () => setIsInstallable(true);\n window.addEventListener('pwa-installable', handleInstallable);\n\n // Load user settings from localStorage\n const savedSettings = localStorage.getItem('nelson-gpt-settings');\n if (savedSettings) {\n try {\n setSettings(JSON.parse(savedSettings));\n } catch (error) {\n console.error('Error loading settings:', error);\n }\n }\n\n // Load user profile from localStorage\n const savedProfile = localStorage.getItem('nelson-gpt-profile');\n if (savedProfile) {\n try {\n setUserProfile(JSON.parse(savedProfile));\n } catch (error) {\n console.error('Error loading profile:', error);\n }\n }\n\n return () => {\n clearTimeout(timer);\n window.removeEventListener('pwa-installable', handleInstallable);\n };\n }, []);\n\n const handleSettingsChange = (newSettings: Partial) => {\n const updatedSettings = { ...settings, ...newSettings };\n setSettings(updatedSettings);\n localStorage.setItem('nelson-gpt-settings', JSON.stringify(updatedSettings));\n };\n\n const handleProfileChange = (newProfile: UserProfile) => {\n setUserProfile(newProfile);\n localStorage.setItem('nelson-gpt-profile', JSON.stringify(newProfile));\n };\n\n const handleInstallPWA = () => {\n if (window.installPWA) {\n window.installPWA();\n setIsInstallable(false);\n }\n };\n\n if (isLoading) {\n return ;\n }\n\n return (\n

\n {/* Sidebar */}\n setIsSidebarOpen(false)}\n settings={settings}\n onSettingsChange={handleSettingsChange}\n userProfile={userProfile}\n onProfileChange={handleProfileChange}\n isInstallable={isInstallable}\n onInstallPWA={handleInstallPWA}\n />\n\n {/* Main Chat Interface */}\n setIsSidebarOpen(true)}\n />\n\n {/* Sidebar Overlay */}\n {isSidebarOpen && (\n setIsSidebarOpen(false)}\n />\n )}\n\n {/* PWA Install Banner */}\n {isInstallable && (\n

Install Nelson-GPT

Get the full app experience

\n setIsInstallable(false)}\n className=\"px-3 py-1 text-sm bg-blue-700 rounded hover:bg-blue-800\"\n >\n Later\n \n \n Install\n \n

\n )}\n

\n );\n};\n\nexport default App;",[],[215,276,302],"d03b11ec-40dc-11f0-b991-76b3cceeab13",["D",1748997757889],[176,177,178,179,180],[215,276],"a788e3b4-40dc-11f0-85cc-76b3cceeab13",["D",1748997689616],[175,181,183,185,186,187,188],[215],"9b942596-40dc-11f0-9b3e-76b3cceeab13","index.ts","9b94269a-40dc-11f0-9b3e-76b3cceeab13","d8c10da7-23cb-47df-969c-82db4d6111a8",8,["D",1748997669558],"import { Hono } from 'https://esm.sh/hono@3.11.7';\nimport { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\nimport { initializeDatabase } from './services/supabase.ts';\nimport chatRoutes from './routes/chat.ts';\nimport embeddingRoutes from './routes/embeddings.ts';\n\nconst app = new Hono();\n\n// Unwrap Hono errors to see original error details\napp.onError((err, c) => {\n throw err;\n});\n\n// Initialize database on startup\ntry {\n await initializeDatabase();\n console.log('Database initialization completed');\n} catch (error) {\n console.error('Database initialization failed:', error);\n}\n\n// API Routes\napp.route('/api/chat', chatRoutes);\napp.route('/api/embeddings', embeddingRoutes);\n\n// Health check\napp.get('/api/health', (c) => {\n return c.json({ \n status: 'healthy',\n service: 'nelson-gpt-api',\n timestamp: new Date().toISOString(),\n version: '1.0.0'\n });\n});\n\n// Serve static files\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n\n// Serve the main application\napp.get(\"/\", async c => {\n try {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n\n // Inject initial data and configuration\n const initialData = {\n apiUrl: '/api',\n version: '1.0.0',\n features: {\n streaming: true,\n offline: true,\n pwa: true\n }\n };\n\n const dataScript = ``;\n\n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n } catch (error) {\n console.error('Error serving main page:', error);\n return c.html(`\n \n \n \n Nelson-GPT - Error\n \n \n \n \n

Nelson-GPT

Sorry, there was an error loading the application.

Error: ${error.message}

\n \n \n `, 500);\n }\n});\n\n// Catch-all for SPA routing\napp.get(\"*\", async c => {\n try {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n const initialData = {\n apiUrl: '/api',\n version: '1.0.0',\n features: {\n streaming: true,\n offline: true,\n pwa: true\n }\n };\n\n const dataScript = ``;\n\n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n } catch (error) {\n return c.notFound();\n }\n});\n\nexport default app;",[],[326,318],"backend","688fbb88-40dc-11f0-a33f-76b3cceeab13","supabase.ts","688fbc78-40dc-11f0-a33f-76b3cceeab13","90242a35-7312-4b01-8aa4-3970242e539b",3,["D",1748997583964],"import { createClient } from 'https://esm.sh/@supabase/supabase-js@2.39.0';\nimport type { NelsonContent, VectorSearchResult } from '../../shared/types.ts';\n\nconst supabaseUrl = Deno.env.get('VITE_SUPABASE_URL')!;\nconst supabaseServiceKey = Deno.env.get('VITE_SUPABASE_SERVICE_ROLE_KEY')!;\n\nexport const supabase = createClient(supabaseUrl, supabaseServiceKey);\n\nexport async function searchSimilarContent(\n embedding: number[],\n limit: number = 5,\n threshold: number = 0.7\n): Promise {\n try {\n const { data, error } = await supabase.rpc('match_nelson_content', {\n query_embedding: embedding,\n match_threshold: threshold,\n match_count: limit\n });\n\n if (error) {\n console.error('Supabase search error:', error);\n throw new Error(`Vector search failed: ${error.message}`);\n }\n\n return data?.map((item: any) => ({\n content: item.content,\n similarity: item.similarity,\n metadata: item.metadata\n })) || [];\n } catch (error) {\n console.error('Error searching similar content:', error);\n throw error;\n }\n}\n\nexport async function insertNelsonContent(\n content: string,\n embedding: number[],\n metadata: {\n chapter: string;\n section: string;\n page: number;\n topic: string;\n }\n): Promise {\n try {\n const { error } = await supabase\n .from('nelson_content')\n .insert({\n content,\n embedding,\n metadata\n });\n\n if (error) {\n console.error('Error inserting content:', error);\n throw new Error(`Failed to insert content: ${error.message}`);\n }\n } catch (error) {\n console.error('Error inserting Nelson content:', error);\n throw error;\n }\n}\n\nexport async function createNelsonContentTable(): Promise {\n try {\n // Create the table if it doesn't exist\n const { error: tableError } = await supabase.rpc('create_nelson_content_table');\n \n if (tableError && !tableError.message.includes('already exists')) {\n console.error('Error creating table:', tableError);\n throw new Error(`Failed to create table: ${tableError.message}`);\n }\n\n // Create the vector similarity search function\n const { error: functionError } = await supabase.rpc('create_match_function');\n \n if (functionError && !functionError.message.includes('already exists')) {\n console.error('Error creating function:', functionError);\n throw new Error(`Failed to create function: ${functionError.message}`);\n }\n } catch (error) {\n console.error('Error setting up database:', error);\n throw error;\n }\n}\n\n// Initialize database schema\nexport async function initializeDatabase(): Promise {\n try {\n // Check if the table exists\n const { data, error } = await supabase\n .from('nelson_content')\n .select('id')\n .limit(1);\n\n if (error && error.code === 'PGRST116') {\n // Table doesn't exist, create it\n await createNelsonContentTable();\n console.log('Database initialized successfully');\n }\n } catch (error) {\n console.error('Error initializing database:', error);\n // Don't throw here, as the app should still work even if DB setup fails\n }\n}",[],[326,248,328],"7d751b9c-40dc-11f0-9f7a-76b3cceeab13","mistral.ts","7d751c64-40dc-11f0-9f7a-76b3cceeab13",5,["D",1748997619023],"import type { \n MistralRequest, \n MistralMessage, \n MistralStreamChunk,\n VectorSearchResult \n} from '../../shared/types.ts';\n\nconst MISTRAL_API_KEY = Deno.env.get('VITE_MISTRAL_API_KEY')!;\nconst MISTRAL_API_URL = 'https://api.mistral.ai/v1/chat/completions';\n\nexport async function generateMistralResponse(\n userMessage: string,\n context: VectorSearchResult[] = [],\n stream: boolean = true\n): Promise {\n try {\n const systemPrompt = createSystemPrompt(context);\n \n const messages: MistralMessage[] = [\n {\n role: 'system',\n content: systemPrompt\n },\n {\n role: 'user',\n content: userMessage\n }\n ];\n\n const requestBody: MistralRequest = {\n model: 'mistral-large-latest',\n messages,\n stream,\n max_tokens: 1000,\n temperature: 0.3\n };\n\n const response = await fetch(MISTRAL_API_URL, {\n method: 'POST',\n headers: {\n 'Authorization': `Bearer ${MISTRAL_API_KEY}`,\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(requestBody),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`Mistral API error: ${response.status} - ${errorText}`);\n }\n\n return response;\n } catch (error) {\n console.error('Error generating Mistral response:', error);\n throw error;\n }\n}\n\nfunction createSystemPrompt(context: VectorSearchResult[]): string {\n const basePrompt = `You are Nelson-GPT, a specialized medical AI assistant powered by the Nelson Textbook of Pediatrics. You provide evidence-based answers to healthcare professionals and medical students focusing on pediatric medicine.\n\nGuidelines:\n- Provide accurate, evidence-based medical information\n- Always cite relevant sections from the Nelson Textbook when available\n- Be clear about limitations and when to seek additional medical consultation\n- Use professional medical terminology while remaining accessible\n- Focus on pediatric medicine and child health\n- Never provide specific treatment recommendations without proper medical evaluation\n- Always recommend consulting with qualified healthcare professionals for patient care decisions\n\nImportant: This is for educational and informational purposes only. Always consult with qualified healthcare professionals for actual patient care.`;\n\n if (context.length > 0) {\n const contextText = context\n .map((item, index) => \n `[Source ${index + 1}] ${item.content}${item.metadata ? ` (Chapter: ${item.metadata.chapter}, Section: ${item.metadata.section})` : ''}`\n )\n .join('\\n\\n');\n\n return `${basePrompt}\n\nRelevant context from Nelson Textbook of Pediatrics:\n${contextText}\n\nPlease use this context to inform your response, citing specific sources when relevant.`;\n }\n\n return basePrompt;\n}\n\nexport async function* streamMistralResponse(response: Response): AsyncGenerator {\n const reader = response.body?.getReader();\n if (!reader) {\n throw new Error('No response body available');\n }\n\n const decoder = new TextDecoder();\n let buffer = '';\n\n try {\n while (true) {\n const { done, value } = await reader.read();\n \n if (done) break;\n \n buffer += decoder.decode(value, { stream: true });\n const lines = buffer.split('\\n');\n buffer = lines.pop() || '';\n\n for (const line of lines) {\n if (line.trim() === '') continue;\n if (line.startsWith('data: ')) {\n const data = line.slice(6);\n if (data === '[DONE]') return;\n \n try {\n const chunk: MistralStreamChunk = JSON.parse(data);\n const content = chunk.choices[0]?.delta?.content;\n if (content) {\n yield content;\n }\n } catch (error) {\n console.error('Error parsing stream chunk:', error);\n // Continue processing other chunks\n }\n }\n }\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport async function generateNonStreamingResponse(\n userMessage: string,\n context: VectorSearchResult[] = []\n): Promise {\n try {\n const response = await generateMistralResponse(userMessage, context, false);\n const data = await response.json();\n \n return data.choices[0]?.message?.content || 'Sorry, I could not generate a response.';\n } catch (error) {\n console.error('Error generating non-streaming response:', error);\n throw error;\n }\n}",[],[326,248,337],"71725404-40dc-11f0-b81a-76b3cceeab13","embeddings.ts","717254ae-40dc-11f0-b81a-76b3cceeab13",4,["D",1748997598873],"import type { EmbeddingRequest, EmbeddingResponse } from '../../shared/types.ts';\n\nconst HUGGINGFACE_API_KEY = Deno.env.get('VITE_HUGGINFACE_API_KEY')!;\nconst EMBEDDING_MODEL = 'sentence-transformers/all-MiniLM-L6-v2';\n\nexport async function generateEmbedding(text: string): Promise {\n try {\n const response = await fetch(\n `https://api-inference.huggingface.co/pipeline/feature-extraction/${EMBEDDING_MODEL}`,\n {\n method: 'POST',\n headers: {\n 'Authorization': `Bearer ${HUGGINGFACE_API_KEY}`,\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n inputs: text,\n options: {\n wait_for_model: true\n }\n }),\n }\n );\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`HuggingFace API error: ${response.status} - ${errorText}`);\n }\n\n const embedding = await response.json();\n \n // Handle different response formats\n if (Array.isArray(embedding) && Array.isArray(embedding[0])) {\n return embedding[0]; // Nested array format\n } else if (Array.isArray(embedding)) {\n return embedding; // Direct array format\n } else {\n throw new Error('Unexpected embedding format from HuggingFace API');\n }\n } catch (error) {\n console.error('Error generating embedding:', error);\n throw new Error(`Failed to generate embedding: ${error.message}`);\n }\n}\n\nexport async function generateEmbeddings(texts: string[]): Promise {\n try {\n const embeddings = await Promise.all(\n texts.map(text => generateEmbedding(text))\n );\n return embeddings;\n } catch (error) {\n console.error('Error generating batch embeddings:', error);\n throw error;\n }\n}\n\n// Utility function to normalize embeddings (optional, for better similarity search)\nexport function normalizeEmbedding(embedding: number[]): number[] {\n const magnitude = Math.sqrt(embedding.reduce((sum, val) => sum + val * val, 0));\n return embedding.map(val => val / magnitude);\n}\n\n// Calculate cosine similarity between two embeddings\nexport function cosineSimilarity(a: number[], b: number[]): number {\n if (a.length !== b.length) {\n throw new Error('Embeddings must have the same length');\n }\n \n let dotProduct = 0;\n let normA = 0;\n let normB = 0;\n \n for (let i = 0; i < a.length; i++) {\n dotProduct += a[i] * b[i];\n normA += a[i] * a[i];\n normB += b[i] * b[i];\n }\n \n return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));\n}",[],[326,248,345],"688fe716-40dc-11f0-a33f-76b3cceeab13",["D",1748997583964],[170,171,172],[326,248],"9214679c-40dc-11f0-80d7-76b3cceeab13","9214686e-40dc-11f0-80d7-76b3cceeab13","e5c9229c-be73-446b-a0b6-7e973ed87fb1",7,["D",1748997653623],"import { Hono } from 'https://esm.sh/hono@3.11.7';\nimport { generateEmbedding, generateEmbeddings } from '../services/embeddings.ts';\nimport { searchSimilarContent } from '../services/supabase.ts';\nimport type { EmbeddingRequest, EmbeddingResponse } from '../../shared/types.ts';\n\nconst embeddings = new Hono();\n\n// Generate embedding for a single text\nembeddings.post('/generate', async (c) => {\n try {\n const body: EmbeddingRequest = await c.req.json();\n const { text } = body;\n\n if (!text?.trim()) {\n return c.json({ error: 'Text is required' }, 400);\n }\n\n const embedding = await generateEmbedding(text);\n\n return c.json({\n embedding,\n dimensions: embedding.length\n } as EmbeddingResponse & { dimensions: number });\n\n } catch (error) {\n console.error('Embedding generation error:', error);\n return c.json({ \n error: 'Failed to generate embedding',\n details: error.message \n }, 500);\n }\n});\n\n// Generate embeddings for multiple texts\nembeddings.post('/batch', async (c) => {\n try {\n const body: { texts: string[] } = await c.req.json();\n const { texts } = body;\n\n if (!Array.isArray(texts) || texts.length === 0) {\n return c.json({ error: 'Texts array is required' }, 400);\n }\n\n if (texts.length > 100) {\n return c.json({ error: 'Maximum 100 texts allowed per batch' }, 400);\n }\n\n const embeddings = await generateEmbeddings(texts);\n\n return c.json({\n embeddings,\n count: embeddings.length,\n dimensions: embeddings[0]?.length || 0\n });\n\n } catch (error) {\n console.error('Batch embedding generation error:', error);\n return c.json({ \n error: 'Failed to generate batch embeddings',\n details: error.message \n }, 500);\n }\n});\n\n// Search similar content using embedding\nembeddings.post('/search', async (c) => {\n try {\n const body: { \n text?: string; \n embedding?: number[]; \n limit?: number; \n threshold?: number; \n } = await c.req.json();\n \n const { text, embedding, limit = 5, threshold = 0.7 } = body;\n\n let searchEmbedding: number[];\n\n if (embedding) {\n searchEmbedding = embedding;\n } else if (text) {\n searchEmbedding = await generateEmbedding(text);\n } else {\n return c.json({ error: 'Either text or embedding is required' }, 400);\n }\n\n const results = await searchSimilarContent(searchEmbedding, limit, threshold);\n\n return c.json({\n results,\n count: results.length,\n query: text || 'embedding provided'\n });\n\n } catch (error) {\n console.error('Embedding search error:', error);\n return c.json({ \n error: 'Failed to search similar content',\n details: error.message \n }, 500);\n }\n});\n\n// Health check endpoint\nembeddings.get('/health', (c) => {\n return c.json({ \n status: 'healthy',\n service: 'nelson-gpt-embeddings',\n timestamp: new Date().toISOString()\n });\n});\n\nexport default embeddings;",[],[326,364,345],"routes","8886a9ec-40dc-11f0-83c9-76b3cceeab13","chat.ts","8886aabe-40dc-11f0-83c9-76b3cceeab13",6,["D",1748997637592],"import { Hono } from 'https://esm.sh/hono@3.11.7';\nimport { streamSSE } from 'https://esm.sh/hono@3.11.7/streaming';\nimport { generateEmbedding } from '../services/embeddings.ts';\nimport { searchSimilarContent } from '../services/supabase.ts';\nimport { generateMistralResponse, streamMistralResponse } from '../services/mistral.ts';\nimport type { ChatRequest, StreamingChatResponse, VectorSearchResult } from '../../shared/types.ts';\n\nconst chat = new Hono();\n\n// Stream chat response\nchat.post('/stream', async (c) => {\n try {\n const body: ChatRequest = await c.req.json();\n const { message, sessionId } = body;\n\n if (!message?.trim()) {\n return c.json({ error: 'Message is required' }, 400);\n }\n\n // Generate embedding for the user message\n const embedding = await generateEmbedding(message);\n \n // Search for relevant content in the Nelson Textbook\n const context = await searchSimilarContent(embedding, 5, 0.7);\n \n // Generate streaming response from Mistral\n const mistralResponse = await generateMistralResponse(message, context, true);\n\n return streamSSE(c, async (stream) => {\n try {\n // Send initial metadata\n await stream.writeSSE({\n data: JSON.stringify({\n type: 'start',\n sessionId: sessionId || crypto.randomUUID(),\n sources: context\n } as StreamingChatResponse),\n event: 'message'\n });\n\n // Stream the response\n for await (const chunk of streamMistralResponse(mistralResponse)) {\n await stream.writeSSE({\n data: JSON.stringify({\n type: 'token',\n content: chunk\n } as StreamingChatResponse),\n event: 'message'\n });\n }\n\n // Send completion signal\n await stream.writeSSE({\n data: JSON.stringify({\n type: 'complete',\n content: '',\n sources: context\n } as StreamingChatResponse),\n event: 'message'\n });\n\n } catch (error) {\n console.error('Streaming error:', error);\n await stream.writeSSE({\n data: JSON.stringify({\n type: 'error',\n content: 'An error occurred while generating the response.'\n } as StreamingChatResponse),\n event: 'error'\n });\n }\n });\n\n } catch (error) {\n console.error('Chat stream error:', error);\n return c.json({ \n error: 'Failed to process chat request',\n details: error.message \n }, 500);\n }\n});\n\n// Non-streaming chat endpoint (fallback)\nchat.post('/', async (c) => {\n try {\n const body: ChatRequest = await c.req.json();\n const { message, sessionId } = body;\n\n if (!message?.trim()) {\n return c.json({ error: 'Message is required' }, 400);\n }\n\n // Generate embedding for the user message\n const embedding = await generateEmbedding(message);\n \n // Search for relevant content\n const context = await searchSimilarContent(embedding, 5, 0.7);\n \n // Generate non-streaming response\n const mistralResponse = await generateMistralResponse(message, context, false);\n const responseData = await mistralResponse.json();\n \n const response = responseData.choices[0]?.message?.content || 'Sorry, I could not generate a response.';\n\n return c.json({\n response,\n sessionId: sessionId || crypto.randomUUID(),\n sources: context\n });\n\n } catch (error) {\n console.error('Chat error:', error);\n return c.json({ \n error: 'Failed to process chat request',\n details: error.message \n }, 500);\n }\n});\n\n// Get chat history (placeholder for future implementation)\nchat.get('/history/:sessionId', async (c) => {\n const sessionId = c.req.param('sessionId');\n \n // TODO: Implement chat history retrieval from database\n return c.json({\n sessionId,\n messages: []\n });\n});\n\n// Health check endpoint\nchat.get('/health', (c) => {\n return c.json({ \n status: 'healthy',\n service: 'nelson-gpt-chat',\n timestamp: new Date().toISOString()\n });\n});\n\nexport default chat;",[],[326,364,366],"8886d53e-40dc-11f0-83c9-76b3cceeab13",["D",1748997637592],[167,168],[326,364],"688fe4fa-40dc-11f0-a33f-76b3cceeab13",["D",1748997583964],[166,169,173],[326],["D",1748997519283],{"_11":12,"_120":70},{"_11":12,"_116":-5,"_69":70,"_31":68,"_117":118,"_119":30},{"_39":107,"_13":16,"_33":36,"_108":74,"_109":-5,"_110":-5,"_111":-5,"_112":-5,"_113":-5,"_114":14,"_115":12},"ctx",{"_52":-5,"_139":418,"_419":420,"_421":422,"_423":30,"_424":30,"_425":30,"_426":24,"_427":-5,"_428":-7,"_429":-7,"_430":26,"_431":30,"_432":30,"_433":-5,"_434":30,"_435":30,"_436":30,"_437":-5},"ENV",{"_389":390,"_391":392,"_393":16,"_394":395,"_396":397,"_398":399,"_400":401,"_402":403,"_404":405,"_406":407,"_408":409,"_410":411,"_412":413,"_414":415,"_416":417},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","RENDER_GIT_COMMIT","71c1b20804b3232a7e513be260cfffc1932a9e97","RENDER_GIT_BRANCH","POSTHOG_API_TOKEN","phc_nmoyuAkStEZuUp69rYlg1DZgrIt19Gx0Y4Hwf8tGgJP","POSTHOG_API_HOST","https://wart.val.town","WEB_API_TEMPLATE","https://{val}.web.val.run","APP_URL","https://www.val.town","MODULE_URL","https://esm.town","VANITY_WEB_API_TEMPLATE","https://{label}.val.run/","DENOLS_URL","https://lsp.val.town","NODE_ENV","production","CLOUDFLARE_TURNSTILE_SITE_KEY","0x4AAAAAACtaIZJH7s9rBMZ6","GOOGLE_CLIENT_ID","875989696899-jtsqkhlbdv5jrlsk8631p7ipjg3c6o62.apps.googleusercontent.com","STRIPE_PUBLISHABLE_KEY","pk_live_51N2zvWEpZTTLipIkVEwXrUG9AT0yeOOEYxszarZT1jpsZV58Ly6wlg3SltZpHN26CUalOBmUSY7adQRQiSkCZz2500qSXNkf8Y","API_URL","https://api.val.town",[],"preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","preferencesCloseBrackets","preferencesChatPanelState","preferencesDprint","preferencesDenofmt","notificationCount","flagDenols","isBusinessOrgMember","businessOrgHandle","isBusinessOrgAdmin","isHigherTownieTier","navCollapsed","currentOrgId"]

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