Error loading page

Please try again later.

\", 500);\n }\n});\n\n// Serve all static files in frontend/ and shared/\napp.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\napp.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n\n// API endpoints\n\n// Health check endpoint\napp.get(\"/health\", c => {\n return c.json({ \n status: \"ok\",\n timestamp: new Date().toISOString(),\n version: projectInfo.version\n });\n});\n\n// Discord interactions endpoint\napp.post(\"/api/interactions\", async c => {\n return await handleDiscordInteractionRequest(c.req.raw);\n});\n\n// Manual trigger for fetching DMs\napp.post(\"/api/fetch-dms\", async c => {\n try {\n log(\"Manually triggering DM fetch...\", \"info\");\n const messages = await fetchAndStoreDMs();\n return c.json({ \n success: true, \n message: \"DM fetch completed successfully\", \n count: messages.length,\n timestamp: new Date().toISOString()\n });\n } catch (error) {\n log(`Error fetching DMs: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return c.json({ \n success: false, \n error: error instanceof Error ? error.message : String(error) \n }, 500);\n }\n});\n\n// Manual trigger for processing links\napp.post(\"/api/process-links\", async c => {\n try {\n log(\"Manually triggering link processing...\", \"info\");\n const result = await batchProcessLinks();\n return c.json({\n ...result,\n timestamp: new Date().toISOString()\n });\n } catch (error) {\n log(`Error processing links: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return c.json({ \n success: false, \n error: error instanceof Error ? error.message : String(error) \n }, 500);\n }\n});\n\n// Query endpoint\napp.post(\"/api/query\", async c => {\n try {\n const body = await c.req.json();\n const { query, filters } = body;\n \n if (!query) {\n return c.json({ success: false, error: \"Missing query parameter\" }, 400);\n }\n \n log(`Processing query: \"${query}\"`, \"info\");\n const result = await processQuery(query, filters);\n \n return c.json({ \n success: true, \n data: result,\n timestamp: new Date().toISOString()\n });\n } catch (error) {\n log(`Error processing query: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return c.json({ \n success: false, \n error: error instanceof Error ? error.message : String(error) \n }, 500);\n }\n});\n\n// Discord command endpoints\napp.post(\"/api/commands/search\", async c => {\n try {\n const body = await c.req.json();\n const { query } = body;\n \n if (!query) {\n return c.json({ success: false, error: \"Missing query parameter\" }, 400);\n }\n \n log(`Processing Discord search command: \"${query}\"`, \"info\");\n \n // Process query with focus on recent messages\n const result = await processQuery(query, { limit: 100 });\n \n // Format response for Discord based on result type\n let response;\n \n switch (result.type) {\n case \"links\":\n if (result.links && result.links.length > 0) {\n response = `Found ${result.links.length} links related to \"${query}\":\\n` +\n result.links.slice(0, 5).map(link => `- ${link.url}`).join(\"\\n\");\n } else {\n response = `No links found related to \"${query}\".`;\n }\n break;\n \n case \"furniture\":\n response = `Furniture Analysis: ${result.analysis || \"No analysis available\"}\\n\\n`;\n if (result.preferences && result.preferences.length > 0) {\n response += `Preferences: ${result.preferences.join(\", \")}\\n\\n`;\n }\n if (result.relevantLinks && result.relevantLinks.length > 0) {\n response += `Relevant Links: ${result.relevantLinks.slice(0, 3).map(link => link.url).join(\", \")}`;\n }\n break;\n \n case \"summary\":\n response = `Summary: ${result.summary || \"No summary available\"}\\n\\n`;\n if (result.keyPoints && result.keyPoints.length > 0) {\n response += `Key Points: ${result.keyPoints.join(\", \")}`;\n }\n break;\n \n default:\n response = result.answer || \"No specific answer found for your query.\";\n }\n \n return c.json({ \n success: true, \n response,\n timestamp: new Date().toISOString()\n });\n } catch (error) {\n log(`Error processing Discord command: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return c.json({ \n success: false, \n response: \"Sorry, I encountered an error processing your request.\" \n }, 500);\n }\n});\n\n// Register commands endpoint\napp.post(\"/api/register-commands\", async c => {\n try {\n const applicationId = getEnv(\"DISCORD_CLIENT_ID\");\n const token = getEnv(\"DISCORD_TOKEN\");\n \n if (!applicationId || !token) {\n return c.json({ \n success: false, \n error: \"Missing Discord credentials in environment variables\" \n }, 400);\n }\n \n log(\"Registering Discord commands...\", \"info\");\n const result = await registerCommands(applicationId, token);\n \n return c.json({ \n success: true, \n message: \"Commands registered successfully\", \n data: result,\n timestamp: new Date().toISOString()\n });\n } catch (error) {\n log(`Error registering commands: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return c.json({ \n success: false, \n error: error instanceof Error ? error.message : String(error) \n }, 500);\n }\n});\n\n// Cron job endpoints for Val.town scheduled execution\n\n/**\n * Handles the scheduled DM fetch cron job\n */\nexport async function cronFetchDMs() {\n try {\n log(\"Running scheduled DM fetch...\", \"info\");\n await initializeServices(); // Ensure services are initialized\n const messages = await fetchAndStoreDMs();\n \n log(`Scheduled DM fetch completed successfully. Fetched ${messages.length} messages.`, \"info\");\n return { \n success: true, \n message: \"Scheduled DM fetch completed successfully\", \n count: messages.length,\n timestamp: new Date().toISOString()\n };\n } catch (error) {\n log(`Error in DM fetch cron job: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return { \n success: false, \n error: error instanceof Error ? error.message : String(error),\n timestamp: new Date().toISOString()\n };\n }\n}\n\n/**\n * Handles the scheduled link processing cron job\n */\nexport async function cronProcessLinks() {\n try {\n log(\"Running scheduled link processing...\", \"info\");\n await initializeServices(); // Ensure services are initialized\n const result = await batchProcessLinks();\n \n log(`Scheduled link processing completed. Processed ${result.processed || 0} links.`, \"info\");\n return {\n ...result,\n timestamp: new Date().toISOString()\n };\n } catch (error) {\n log(`Error in link processing cron job: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return { \n success: false, \n error: error instanceof Error ? error.message : String(error),\n timestamp: new Date().toISOString()\n };\n }\n}\n\n// Expose cron endpoints via HTTP as well\napp.get(\"/api/cron/fetch-dms\", async c => {\n const result = await cronFetchDMs();\n return c.json(result, result.success ? 200 : 500);\n});\n\napp.get(\"/api/cron/process-links\", async c => {\n const result = await cronProcessLinks();\n return c.json(result, result.success ? 200 : 500);\n});\n\n// Error handler - Unwrap Hono errors to see original error details\napp.onError((err, c) => {\n log(`Unhandled server error: ${err.message}`, \"error\");\n throw err;\n});\n\n// Export the fetch handler for Val.town HTTP vals\nexport default app.fetch;",[],[151,169,222],{"_107":230,"_100":231,"_152":232,"_154":170,"_155":139,"_156":233,"_158":120,"_109":234,"_104":31,"_160":180,"_162":235,"_163":27,"_164":-5,"_165":236,"_103":237},"7cf17d22-2710-11f0-818e-569c3dd06744","link-analyzer.ts","7cf17de0-2710-11f0-818e-569c3dd06744",25,["D",1746161221514],"// Link analyzer for Val.town\nimport { getMessages, updateLinkCategory } from \"./database.ts\";\nimport { categorizeContent } from \"./anthropic-service.ts\";\nimport { extractUrls, log } from \"../shared/utils.ts\";\nimport type { MessageRecord, LinkRecord } from \"../shared/types.ts\";\n\ninterface LinkToProcess {\n url: string;\n messageId: string;\n authorId: string;\n timestamp: number;\n messageContent: string;\n id?: number;\n}\n\ninterface CategoryResult {\n category: string;\n confidence: 'high' | 'medium' | 'low';\n isValidCategory: boolean;\n suggestedCategory?: string;\n error?: string;\n}\n\ninterface MetadataResult {\n title: string;\n description: string;\n}\n\n/**\n * Extract and process links from messages\n * @param messages - Array of message objects\n * @returns Array of processed link objects\n */\nexport function extractLinks(messages: MessageRecord[]): LinkToProcess[] {\n const extractedLinks: LinkToProcess[] = [];\n \n for (const message of messages) {\n if (!message.content) continue;\n \n const urls = extractUrls(message.content);\n if (!urls) continue;\n \n for (const url of urls) {\n extractedLinks.push({\n url,\n messageId: message.id,\n authorId: message.author_id,\n timestamp: typeof message.timestamp === 'string' \n ? parseInt(message.timestamp) \n : message.timestamp,\n messageContent: message.content,\n id: message.id ? parseInt(message.id) : undefined\n });\n }\n }\n \n return extractedLinks;\n}\n\n/**\n * Categorize a link using AI\n * @param link - Link object to categorize\n * @returns Categorization result\n */\nexport async function categorizeLink(link: LinkToProcess): Promise {\n try {\n // For furniture-specific project, check for furniture-related terms\n const furnitureTerms = [\n 'sofa', 'couch', 'chair', 'table', 'bed', 'dresser', 'nightstand',\n 'desk', 'shelf', 'bookcase', 'cabinet', 'wardrobe', 'ottoman',\n 'bench', 'stool', 'lamp', 'lighting', 'decor', 'furniture'\n ];\n \n // Check if URL or message context contains furniture terms\n const urlLower = link.url.toLowerCase();\n const messageLower = link.messageContent ? link.messageContent.toLowerCase() : '';\n \n let isFurnitureRelated = false;\n const detectedTerms: string[] = [];\n \n for (const term of furnitureTerms) {\n if (urlLower.includes(term) || messageLower.includes(term)) {\n isFurnitureRelated = true;\n detectedTerms.push(term);\n }\n }\n \n if (isFurnitureRelated) {\n // Use Anthropic to determine specific furniture category\n const validCategories = [\n 'sofa', 'chair', 'table', 'bed', 'dresser', 'desk', \n 'lamp', 'shelf', 'bookcase', 'rug', 'curtain', 'other'\n ];\n \n const contentToAnalyze = `\nURL: ${link.url}\nMessage context: ${link.messageContent}\nDetected furniture terms: ${detectedTerms.join(', ')}\n `;\n \n const category = await categorizeContent(contentToAnalyze, validCategories);\n \n // Validate returned category\n if (validCategories.includes(category)) {\n return {\n category,\n confidence: 'high',\n isValidCategory: true\n };\n } else {\n return {\n category: 'other',\n confidence: 'low',\n isValidCategory: false,\n suggestedCategory: category\n };\n }\n } else {\n // Not furniture-related\n return {\n category: 'other',\n confidence: 'high',\n isValidCategory: true\n };\n }\n } catch (error) {\n log(`Error categorizing link: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return {\n category: 'other',\n confidence: 'low',\n isValidCategory: true,\n error: error instanceof Error ? error.message : String(error)\n };\n }\n}\n\n/**\n * Extract metadata for a link (title, description)\n * @param url - URL to extract metadata for\n * @returns Metadata object\n */\nexport async function extractLinkMetadata(url: string): Promise {\n try {\n // In a production system, you'd want to actually fetch the page content\n // For simplicity, we'll make an educated guess based on the URL\n \n // Parse the URL\n const urlObj = new URL(url);\n const hostname = urlObj.hostname;\n const pathname = urlObj.pathname;\n \n // Extract potential title from pathname\n let title = '';\n if (pathname && pathname !== '/') {\n // Remove extensions and convert to title case\n const parts = pathname.split('/').filter(p => p && !p.startsWith('?'));\n if (parts.length > 0) {\n // Get the last part of the path, which often contains the title\n const lastPart = parts[parts.length - 1]\n .replace(/\\.html|\\.php|\\.asp|\\.aspx|\\.jsp/gi, '')\n .replace(/-|_/g, ' ');\n \n // Convert to title case\n title = lastPart\n .split(' ')\n .map(word => word.charAt(0).toUpperCase() + word.slice(1).toLowerCase())\n .join(' ');\n }\n }\n \n // If we couldn't extract a title, use the domain name\n if (!title) {\n // Remove www. and .com, .org, etc.\n title = hostname\n .replace(/^www\\./i, '')\n .replace(/\\.(com|org|net|io|gov|edu|co|uk|us|ca|au|de|fr|jp|cn|ru|br|in)$/i, '')\n .split('.')\n .map(part => part.charAt(0).toUpperCase() + part.slice(1))\n .join(' ');\n }\n \n // Create a simple description\n const description = `Content from ${hostname}${pathname}`;\n \n return {\n title: title || hostname,\n description: description\n };\n } catch (error) {\n log(`Error extracting link metadata: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n try {\n return {\n title: new URL(url).hostname,\n description: 'No description available'\n };\n } catch {\n return {\n title: \"Unknown domain\",\n description: \"Invalid URL format\"\n };\n }\n }\n}\n\n/**\n * Batch process all links in the database\n * - Categorizes uncategorized links\n * - Updates link metadata\n */\nexport async function batchProcessLinks(): Promise<{success: boolean, processed?: number, error?: string}> {\n try {\n // Get messages with links that haven't been processed\n const messages = await getMessages({ limit: 100 });\n \n // Extract links\n const links = extractLinks(messages);\n \n let processedCount = 0;\n \n // Process each link\n for (const link of links) {\n try {\n // Skip links without ID\n if (!link.id) continue;\n \n // Categorize the link\n const categorization = await categorizeLink(link);\n \n // Extract metadata\n const metadata = await extractLinkMetadata(link.url);\n \n // Update link in database\n await updateLinkCategory(link.id, categorization.category);\n \n log(`Processed link: ${link.url}`, \"info\");\n log(`Category: ${categorization.category}`, \"info\");\n log(`Title: ${metadata.title}`, \"info\");\n \n processedCount++;\n } catch (linkError) {\n log(`Error processing link ${link.url}: ${linkError instanceof Error ? linkError.message : String(linkError)}`, \"error\");\n // Continue with next link\n }\n }\n \n return {\n success: true,\n processed: processedCount\n };\n } catch (error) {\n log(`Error in batch processing links: ${error instanceof Error ? error.message : String(error)}`, \"error\");\n return {\n success: false,\n error: error instanceof Error ? error.message : String(error)\n };\n }\n}",[],[151,169,231],{"_107":239,"_100":240,"_152":241,"_154":170,"_155":139,"_156":242,"_158":120,"_109":243,"_104":31,"_160":180,"_162":244,"_163":27,"_164":-5,"_165":245,"_103":246},"7ccc0736-2710-11f0-89d1-569c3dd06744","query-service.ts","7ccc07d6-2710-11f0-89d1-569c3dd06744",15,["D",1746161221452],"// Query service for Val.town\nimport { getMessages, getLinks } from \"./database.ts\";\nimport { analyzeConversation, summarizeConversation } from \"./anthropic-service.ts\";\n\n/**\n * Process a user query about messages and links\n * @param {string} query - The user's query\n * @param {Object} filters - Optional filters for message retrieval\n * @returns {Object} - The query response\n */\nexport async function processQuery(query: string, filters: any = {}) {\n try {\n // Analyze the query to determine intent\n const queryIntent = await analyzeQueryIntent(query);\n \n // Handle different types of queries based on intent\n switch (queryIntent.type) {\n case 'link_search':\n return await handleLinkSearch(query, queryIntent.categories);\n \n case 'conversation_search':\n return await handleConversationSearch(query, filters);\n \n case 'summary_request':\n return await handleSummaryRequest(query, filters);\n \n case 'furniture_specific':\n return await handleFurnitureQuery(query);\n \n default:\n // General query - use AI to analyze messages\n return await handleGeneralQuery(query, filters);\n }\n } catch (error) {\n console.error('Error processing query:', error);\n throw error;\n }\n}\n\n/**\n * Analyze the intent of a user query\n * @param {string} query - The user's query\n * @returns {Object} - The query intent\n */\nasync function analyzeQueryIntent(query: string) {\n try {\n const prompt = `\nDetermine the intent of this query about Discord messages between two people:\n\"${query}\"\n\nClassify it as one of these types:\n1. link_search - looking for shared links\n2. conversation_search - looking for specific conversation topics\n3. summary_request - wanting summaries of conversations\n4. furniture_specific - specifically about furniture\n5. general_query - any other type of question\n\nIf it's link_search or furniture_specific, also identify relevant categories.\n\nResponse format:\n{\n \"type\": \"intent_type\",\n \"categories\": [\"category1\", \"category2\"] // Only for link_search or furniture_specific\n}\n `;\n \n const response = await analyzeConversation([], prompt);\n \n // Try to extract JSON from the response\n const jsonMatch = response.match(/\\{[\\s\\S]*\\}/);\n if (jsonMatch) {\n try {\n return JSON.parse(jsonMatch[0]);\n } catch (e) {\n console.error('Error parsing intent JSON:', e);\n }\n }\n \n // Simple heuristic fallback\n const lowerQuery = query.toLowerCase();\n if (lowerQuery.includes('link') || lowerQuery.includes('url') || lowerQuery.includes('website')) {\n return { type: 'link_search', categories: [] };\n } else if (lowerQuery.includes('sofa') || lowerQuery.includes('chair') || \n lowerQuery.includes('table') || lowerQuery.includes('furniture')) {\n return { type: 'furniture_specific', categories: ['sofa', 'chair', 'table'] };\n } else if (lowerQuery.includes('summary') || lowerQuery.includes('summarize')) {\n return { type: 'summary_request', categories: [] };\n } else {\n return { type: 'general_query', categories: [] };\n }\n } catch (error) {\n console.error('Error analyzing query intent:', error);\n return { \n type: 'general_query',\n categories: []\n };\n }\n}\n\n/**\n * Handle a link search query\n * @param {string} query - The user's query\n * @param {Array} categories - Categories to search in\n * @returns {Object} - The query response\n */\nasync function handleLinkSearch(query: string, categories: string[] = []) {\n let linkFilters: any = {};\n \n // If categories are provided, use them\n if (categories && categories.length > 0) {\n linkFilters.category = categories;\n }\n \n // Get links based on filters\n const links = getLinks(linkFilters);\n \n // If no specific categories, use Anthropic to help filter relevant links\n if ((!categories || categories.length === 0) && links.length > 0) {\n const relevantLinks = await filterRelevantLinks(links, query);\n return {\n type: 'links',\n links: relevantLinks,\n message: `Found ${relevantLinks.length} links that might be relevant to your query.`\n };\n }\n \n return {\n type: 'links',\n links: links,\n message: `Found ${links.length} links in the specified categories.`\n };\n}\n\n/**\n * Filter links for relevance to a query\n * @param {Array} links - Array of link objects\n * @param {string} query - The user's query\n * @returns {Array} - Filtered array of relevant links\n */\nasync function filterRelevantLinks(links: any[], query: string) {\n if (links.length === 0) return [];\n \n try {\n // Prepare a sample of links (limit to avoid token limits)\n const linkSample = links.slice(0, 50).map(link => ({\n url: link.url,\n content: link.content,\n id: link.id\n }));\n \n const prompt = `\nI have a list of links shared in Discord messages. Based on this query: \"${query}\"\nPlease determine which of these links are most relevant:\n\n${JSON.stringify(linkSample, null, 2)}\n\nReturn only the IDs of relevant links in a JSON array format like:\n[\"1\", \"5\", \"10\"]\n `;\n \n const response = await analyzeConversation([], prompt);\n \n // Try to extract JSON array from the response\n const jsonMatch = response.match(/\\[([\\s\\S]*?)\\]/);\n if (jsonMatch) {\n try {\n const relevantIds = JSON.parse(jsonMatch[0]);\n return links.filter(link => relevantIds.includes(link.id.toString()));\n } catch (e) {\n console.error('Error parsing relevant link IDs:', e);\n }\n }\n \n // Simple keyword matching fallback\n return links.filter(link => {\n const lowerUrl = link.url.toLowerCase();\n const lowerContent = (link.content || '').toLowerCase();\n const lowerQuery = query.toLowerCase();\n \n const queryKeywords = lowerQuery.split(/\\s+/).filter((word: string) => word.length > 3);\n \n return queryKeywords.some(keyword => \n lowerUrl.includes(keyword) || lowerContent.includes(keyword)\n );\n });\n } catch (error) {\n console.error('Error filtering relevant links:', error);\n return links;\n }\n}\n\n/**\n * Handle a conversation search query\n * @param {string} query - The user's query\n * @param {Object} filters - Message filters\n * @returns {Object} - The query response\n */\nasync function handleConversationSearch(query: string, filters: any = {}) {\n // Get messages based on filters\n const messages = getMessages(filters);\n \n try {\n // Use Anthropic to analyze the conversation\n const result = await analyzeConversation(messages.slice(0, 50), query);\n \n // Extract relevant messages if possible\n const relevantMessageIds: string[] = [];\n const relevantMessages: any[] = [];\n \n // Try to find message IDs in the response\n const messageIdRegex = /message\\s+(?:ID|id|Id):\\s*([a-zA-Z0-9]+)/g;\n let match;\n while ((match = messageIdRegex.exec(result)) !== null) {\n relevantMessageIds.push(match[1]);\n }\n \n // If we found message IDs, look them up\n if (relevantMessageIds.length > 0) {\n for (const id of relevantMessageIds) {\n const message = messages.find(m => m.id === id);\n if (message) {\n relevantMessages.push({\n id: message.id,\n content: message.content,\n timestamp: message.timestamp,\n context: \"Relevant message from conversation\"\n });\n }\n }\n }\n \n return {\n type: 'conversation',\n relevantMessages: relevantMessages,\n summary: result\n };\n } catch (error) {\n console.error('Error in conversation search:', error);\n return {\n type: 'error',\n message: 'Error processing conversation search.'\n };\n }\n}\n\n/**\n * Handle a summary request\n * @param {string} query - The user's query\n * @param {Object} filters - Message filters\n * @returns {Object} - The query response\n */\nasync function handleSummaryRequest(query: string, filters: any = {}) {\n // Get messages based on filters\n const messages = getMessages(filters);\n \n // Use Anthropic to generate a summary\n try {\n const summaryResult = await summarizeConversation(messages.slice(0, 100), query);\n \n // Extract key points if possible\n const keyPointsRegex = /Key points:([\\s\\S]*?)(?:\\n\\n|$)/i;\n const keyPointsMatch = summaryResult.summary.match(keyPointsRegex);\n \n let keyPoints: string[] = [];\n if (keyPointsMatch) {\n // Extract bullet points\n const bulletPointRegex = /[-*]\\s*(.+?)(?=\\n[-*]|\\n\\n|$)/g;\n let bulletMatch;\n while ((bulletMatch = bulletPointRegex.exec(keyPointsMatch[1])) !== null) {\n keyPoints.push(bulletMatch[1].trim());\n }\n }\n \n // If no key points were extracted using regex, try to infer some\n if (keyPoints.length === 0) {\n const sentences = summaryResult.summary.split(/[.!?]+/).filter((s: string) => s.trim().length > 20);\n keyPoints = sentences.slice(0, 3).map((s: string) => s.trim());\n }\n \n return {\n type: 'summary',\n summary: summaryResult.summary,\n keyPoints: keyPoints,\n messageCount: messages.length\n };\n } catch (error) {\n console.error('Error generating summary:', error);\n return {\n type: 'error',\n message: 'Error generating summary.'\n };\n }\n}\n\n/**\n * Handle a furniture-specific query\n * @param {string} query - The user's query\n * @returns {Object} - The query response\n */\nasync function handleFurnitureQuery(query: string) {\n // Get furniture-related links\n const furnitureCategories = ['sofa', 'chair', 'table', 'bed', 'dresser', 'desk', 'lamp'];\n const furnitureLinks = getLinks({ category: furnitureCategories });\n \n // Get messages that mention furniture\n const furnitureKeywords = ['sofa', 'chair', 'table', 'bed', 'dresser', 'desk', 'lamp', 'couch', 'furniture'];\n \n let messagesWithFurniture: any[] = [];\n for (const keyword of furnitureKeywords) {\n const messages = getMessages({ search: keyword });\n messagesWithFurniture = [...messagesWithFurniture, ...messages];\n }\n \n // Remove duplicates\n const uniqueMessageIds = new Set();\n messagesWithFurniture = messagesWithFurniture.filter(msg => {\n if (uniqueMessageIds.has(msg.id)) {\n return false;\n }\n uniqueMessageIds.add(msg.id);\n return true;\n });\n \n // Use Anthropic to analyze furniture-related content\n try {\n // Create prompt for furniture analysis\n const prompt = `\nI have a set of Discord messages and links related to furniture. The user wants to know about: \"${query}\"\n\nPlease analyze what the two people have discussed about furniture,\nespecially anything related to the user's query. Include any preferences, opinions,\nor decisions they've made about furniture items.\n\nFocus on:\n1. Overall furniture preferences and tastes\n2. Specific opinions about furniture items they've shared\n3. Any decisions they've made about furniture purchases\n4. Finding relevant furniture links that address the query\n `;\n \n const analysis = await analyzeConversation(messagesWithFurniture.slice(0, 50), prompt);\n \n // Extract preferences if possible\n const preferencesRegex = /preferences:?([\\s\\S]*?)(?:\\n\\n|$)/i;\n const preferencesMatch = analysis.match(preferencesRegex);\n \n let preferences: string[] = [];\n if (preferencesMatch) {\n // Extract bullet points\n const bulletPointRegex = /[-*]\\s*(.+?)(?=\\n[-*]|\\n\\n|$)/g;\n let bulletMatch;\n while ((bulletMatch = bulletPointRegex.exec(preferencesMatch[1])) !== null) {\n preferences.push(bulletMatch[1].trim());\n }\n }\n \n // If no preferences were extracted, try to infer some\n if (preferences.length === 0) {\n // Look for phrases like \"prefers\", \"likes\", \"wants\", etc.\n const preferenceRegex = /(?:prefer|like|want|look for|interested in)s?\\s+([^.!?]+)/gi;\n let prefMatch;\n while ((prefMatch = preferenceRegex.exec(analysis)) !== null) {\n preferences.push(prefMatch[1].trim());\n }\n }\n \n // Find relevant links\n const relevantLinks = furnitureLinks.slice(0, 10).map(link => ({\n url: link.url,\n context: `Shared furniture link related to ${link.category || 'furniture'}`\n }));\n \n return {\n type: 'furniture',\n analysis: analysis,\n preferences: preferences.slice(0, 5), // Limit to 5 preferences\n relevantLinks: relevantLinks,\n messageCount: messagesWithFurniture.length,\n linkCount: furnitureLinks.length\n };\n } catch (error) {\n console.error('Error analyzing furniture data:', error);\n return {\n type: 'error',\n message: 'Error analyzing furniture-related data.'\n };\n }\n}\n\n/**\n * Handle a general query\n * @param {string} query - The user's query\n * @param {Object} filters - Message filters\n * @returns {Object} - The query response\n */\nasync function handleGeneralQuery(query: string, filters: any = {}) {\n // Get messages based on filters\n const messages = getMessages(filters);\n \n // Use Anthropic to analyze messages\n try {\n // Direct analysis of messages with Claude\n const answer = await analyzeConversation(messages.slice(0, 50), query);\n \n // Extract evidence if possible\n const evidenceRegex = /evidence:?([\\s\\S]*?)(?:\\n\\n|$)/i;\n const evidenceMatch = answer.match(evidenceRegex);\n \n let evidence: string[] = [];\n if (evidenceMatch) {\n // Extract bullet points\n const bulletPointRegex = /[-*]\\s*(.+?)(?=\\n[-*]|\\n\\n|$)/g;\n let bulletMatch;\n while ((bulletMatch = bulletPointRegex.exec(evidenceMatch[1])) !== null) {\n evidence.push(bulletMatch[1].trim());\n }\n }\n \n // Determine confidence based on answer content\n let confidence = 'medium';\n if (answer.toLowerCase().includes('not enough information') || \n answer.toLowerCase().includes('cannot determine') ||\n answer.toLowerCase().includes('no evidence')) {\n confidence = 'low';\n } else if (answer.toLowerCase().includes('clearly') || \n answer.toLowerCase().includes('definitely') ||\n answer.toLowerCase().includes('conclusively')) {\n confidence = 'high';\n }\n \n return {\n type: 'general',\n answer: answer,\n evidence: evidence,\n confidence: confidence,\n messageCount: messages.length\n };\n } catch (error) {\n console.error('Error processing general query:', error);\n return {\n type: 'error',\n message: 'Error processing your query.'\n };\n }\n}",[],[151,169,240],[151,169],{"_107":249,"_100":250,"_152":251,"_154":153,"_155":139,"_156":252,"_158":120,"_109":253,"_104":31,"_160":161,"_162":-5,"_163":27,"_164":-5,"_165":254,"_103":291},"7c532f96-2710-11f0-8393-569c3dd06744","frontend","7c533040-2710-11f0-8393-569c3dd06744",2,["D",1746161220722],[255,272,282],{"_107":256,"_100":257,"_152":258,"_154":251,"_155":139,"_156":259,"_158":120,"_109":260,"_104":31,"_160":161,"_162":-5,"_163":27,"_164":-5,"_165":261,"_103":271},"7c86d184-2710-11f0-9826-569c3dd06744","components","7c86d210-2710-11f0-9826-569c3dd06744",5,["D",1746161221058],[262],{"_107":263,"_100":264,"_152":265,"_154":258,"_155":139,"_156":266,"_158":120,"_109":267,"_104":31,"_160":180,"_162":268,"_163":27,"_164":-5,"_165":269,"_103":270},"7cc7fefc-2710-11f0-9826-569c3dd06744","App.tsx","7cc7ffba-2710-11f0-9826-569c3dd06744",14,["D",1746161221385],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect } from \"https://esm.sh/react@18.2.0\";\n\ninterface ProjectInfo {\n name: string;\n username: string;\n version: string;\n viewSourceUrl: string;\n}\n\n// Access the project info injected by the server\ndeclare global {\n interface Window {\n __PROJECT_INFO__?: ProjectInfo;\n }\n}\n\nexport function App() {\n const [status, setStatus] = useState<'idle' | 'loading' | 'success' | 'error'>('idle');\n const [serverInfo, setServerInfo] = useState(null);\n const [error, setError] = useState(null);\n \n // Fetch server health status on component mount\n useEffect(() => {\n async function checkHealth() {\n try {\n setStatus('loading');\n const response = await fetch('/health');\n if (!response.ok) {\n throw new Error(`Health check failed: ${response.status}`);\n }\n const data = await response.json();\n setServerInfo(data);\n setStatus('success');\n } catch (err) {\n console.error('Health check error:', err);\n setError(err instanceof Error ? err.message : String(err));\n setStatus('error');\n }\n }\n \n checkHealth();\n }, []);\n \n // Get project info from window object\n const projectInfo = window.__PROJECT_INFO__;\n \n return (\n

Discord DM Analyzer

\n AI-powered tool to analyze Discord DMs, track shared links, and provide insights about conversations.\n

\n \n

Server Status

\n {status === 'loading' && (\n

Checking server status...

\n )}\n \n {status === 'error' && (\n

Error connecting to server

\n {error &&

{error}

}\n

\n )}\n \n {status === 'success' && serverInfo && (\n

Status

\n \n {serverInfo.status}\n

\n {serverInfo.version && (\n

Version

{serverInfo.version}

\n )}\n {serverInfo.timestamp && (\n

Last Check

\n {new Date(serverInfo.timestamp).toLocaleString()}\n

\n )}\n

\n \n

Available Endpoints

\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 \n

Endpoint	Method	Description
/api/interactions	POST	Discord interactions endpoint
/api/fetch-dms	POST	Manually trigger DM fetching
/api/process-links	POST	Manually process links
/api/query	POST	Query your conversation data
/api/register-commands	POST	Register Discord slash commands

\n \n \n

\n );\n}",[],[151,250,257,264],[151,250,257],{"_107":273,"_100":274,"_152":275,"_154":251,"_155":139,"_156":276,"_158":120,"_109":277,"_104":31,"_160":278,"_162":279,"_163":27,"_164":-5,"_165":280,"_103":281},"7cbacb6a-2710-11f0-8393-569c3dd06744","index.html","7cbacc1e-2710-11f0-8393-569c3dd06744",10,["D",1746161221383],"file","\n\n \n \n \n Discord DM Analyzer\n \n \n \n \n \n \n

\n \n \n \n",[],[151,250,274],{"_107":283,"_100":284,"_152":285,"_154":251,"_155":139,"_156":286,"_158":120,"_109":287,"_104":31,"_160":180,"_162":288,"_163":27,"_164":-5,"_165":289,"_103":290},"7cb7fd86-2710-11f0-a1a2-569c3dd06744","index.tsx","7cb7fe1c-2710-11f0-a1a2-569c3dd06744",9,["D",1746161221382],"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { createRoot } from \"https://esm.sh/react-dom@18.2.0/client?deps=react@18.2.0\";\nimport { App } from \"./components/App.tsx\";\n\n// Get root element\nconst root = document.getElementById(\"root\");\nif (!root) {\n throw new Error(\"Root element not found in document\");\n}\n\n// Render the App\ncreateRoot(root).render();",[],[151,250,284],[151,250],{"_107":293,"_100":294,"_152":295,"_154":153,"_155":139,"_156":296,"_158":120,"_109":297,"_104":31,"_160":161,"_162":-5,"_163":27,"_164":-5,"_165":298,"_103":317},"7c75e126-2710-11f0-8393-569c3dd06744","shared","7c75e1b2-2710-11f0-8393-569c3dd06744",4,["D",1746161220949],[299,308],{"_107":300,"_100":301,"_152":302,"_154":295,"_155":139,"_156":303,"_158":120,"_109":304,"_104":31,"_160":180,"_162":305,"_163":27,"_164":-5,"_165":306,"_103":307},"7ce652f8-2710-11f0-89d1-569c3dd06744","types.ts","7ce653ac-2710-11f0-89d1-569c3dd06744",22,["D",1746161221561],"/**\n * Shared types for Discord DM Analyzer\n */\n\n// Discord-related types\nexport interface DiscordMessage {\n id: string;\n content: string;\n authorId: string;\n timestamp: number;\n attachments?: string[];\n embeds?: string[];\n}\n\nexport interface DiscordChannel {\n id: string;\n type: number;\n name?: string;\n recipient_id?: string;\n}\n\nexport interface DiscordInteraction {\n id: string;\n type: number;\n data: {\n id?: string;\n name?: string;\n options?: DiscordInteractionOption[];\n };\n guild_id?: string;\n channel_id?: string;\n user?: {\n id: string;\n username: string;\n };\n token: string;\n version: number;\n}\n\nexport interface DiscordInteractionOption {\n name: string;\n type: number;\n value?: string;\n options?: DiscordInteractionOption[];\n}\n\nexport interface DiscordInteractionResponse {\n type: number;\n data?: {\n content?: string;\n embeds?: any[];\n components?: any[];\n flags?: number;\n };\n}\n\n// Database-related types\nexport interface MessageRecord {\n id: string;\n content: string;\n author_id: string;\n timestamp: number;\n processed: number;\n attachment_url?: string;\n}\n\nexport interface LinkRecord {\n id: number;\n message_id: string;\n url: string;\n category: string;\n title?: string;\n description?: string;\n content?: string;\n timestamp?: number;\n author_id?: string;\n}\n\nexport interface CategoryRecord {\n id: number;\n name: string;\n created_at: number;\n}\n\n// Query-related types\nexport interface QueryFilters {\n startDate?: string | number;\n endDate?: string | number;\n authorId?: string;\n search?: string;\n limit?: number;\n category?: string | string[];\n url?: string;\n}\n\nexport interface QueryResult {\n type: 'links' | 'conversation' | 'summary' | 'furniture' | 'general' | 'error';\n links?: LinkRecord[];\n message?: string;\n relevantMessages?: MessageRecord[];\n summary?: string;\n analysis?: string;\n preferences?: string[];\n relevantLinks?: { url: string; context: string }[];\n messageCount?: number;\n linkCount?: number;\n keyPoints?: string[];\n answer?: string;\n evidence?: string[];\n confidence?: 'low' | 'medium' | 'high';\n error?: string;\n}\n\n// Claude API types\nexport interface ClaudeOptions {\n model?: string;\n maxTokens?: number;\n temperature?: number;\n systemPrompt?: string;\n}\n\nexport interface SummaryResult {\n summary: string;\n messageCount: number;\n}",[],[151,294,301],{"_107":309,"_100":310,"_152":311,"_154":295,"_155":139,"_156":312,"_158":120,"_109":313,"_104":31,"_160":180,"_162":314,"_163":27,"_164":-5,"_165":315,"_103":316},"7cdea8f0-2710-11f0-a023-569c3dd06744","utils.ts","7cdea9ae-2710-11f0-a023-569c3dd06744",20,["D",1746161221555],"/**\n * Shared utility functions for Discord DM Analyzer\n */\n\n/**\n * Extract URLs from text content\n * @param content - The text content to extract URLs from\n * @returns Array of matched URLs or null if none found\n */\nexport function extractUrls(content: string): string[] | null {\n if (!content) return null;\n \n // Simple URL regex for extraction\n const urlRegex = /(https?:\\/\\/[^\\s]+)/g;\n const matches = content.match(urlRegex);\n \n return matches;\n}\n\n/**\n * Format timestamp to human-readable date string\n * @param timestamp - Unix timestamp in milliseconds\n * @returns Formatted date string\n */\nexport function formatTimestamp(timestamp: number): string {\n return new Date(timestamp).toLocaleString();\n}\n\n/**\n * Create a proper Discord command response\n * @param content - Response content\n * @param ephemeral - Whether the response should be ephemeral (only visible to command user)\n * @returns Discord interaction response object\n */\nexport function createCommandResponse(content: string, ephemeral: boolean = false): any {\n return {\n type: 4, // CHANNEL_MESSAGE_WITH_SOURCE\n data: {\n content,\n flags: ephemeral ? 64 : undefined // EPHEMERAL = 64\n }\n };\n}\n\n/**\n * Truncate text to specified length with ellipsis\n * @param text - Text to truncate\n * @param maxLength - Maximum length\n * @returns Truncated text\n */\nexport function truncateText(text: string, maxLength: number = 1000): string {\n if (!text || text.length <= maxLength) return text;\n return text.substring(0, maxLength - 3) + '...';\n}\n\n/**\n * Safely parse JSON with fallback\n * @param text - Text to parse as JSON\n * @param fallback - Fallback value if parsing fails\n * @returns Parsed JSON or fallback\n */\nexport function safeJsonParse(text: string, fallback: any = null): any {\n try {\n return JSON.parse(text);\n } catch (error) {\n return fallback;\n }\n}\n\n/**\n * Split array into chunks of specified size\n * @param array - Array to split\n * @param chunkSize - Size of each chunk\n * @returns Array of chunks\n */\nexport function chunkArray(array: T[], chunkSize: number): T[][] {\n const chunks = [];\n for (let i = 0; i < array.length; i += chunkSize) {\n chunks.push(array.slice(i, i + chunkSize));\n }\n return chunks;\n}\n\n/**\n * Get environment variable with validation\n * @param key - Environment variable key\n * @param required - Whether the variable is required\n * @returns Environment variable value or undefined\n */\nexport function getEnv(key: string, required: boolean = true): string | undefined {\n const value = Deno.env.get(key);\n if (required && !value) {\n console.error(`Required environment variable ${key} is missing`);\n }\n return value;\n}\n\n/**\n * Log message with timestamp\n * @param message - Message to log\n * @param level - Log level\n */\nexport function log(message: string, level: 'info' | 'error' | 'warn' | 'debug' = 'info'): void {\n const timestamp = new Date().toISOString();\n const formattedMessage = `[${timestamp}] [${level.toUpperCase()}] ${message}`;\n \n switch (level) {\n case 'error':\n console.error(formattedMessage);\n break;\n case 'warn':\n console.warn(formattedMessage);\n break;\n case 'debug':\n console.debug(formattedMessage);\n break;\n default:\n console.log(formattedMessage);\n }\n}",[],[151,294,310],[151,294],{"_107":319,"_100":320,"_152":321,"_154":153,"_155":139,"_156":322,"_158":120,"_109":323,"_104":31,"_160":278,"_162":324,"_163":27,"_164":-5,"_165":325,"_103":326},"7cdaa462-2710-11f0-9826-569c3dd06744","README.md","7cdaa4f8-2710-11f0-9826-569c3dd06744",19,["D",1746161221586],"# Discord DM Analyzer for Val.town\n\nThis project is a Discord bot that analyzes direct messages between two users (e.g., you and your partner), particularly focusing on:\n\n1. Tracking and categorizing shared links\n2. Searching conversation history\n3. Summarizing discussions on specific topics\n4. Analyzing furniture-related discussions\n\n## Architecture\n\nThe project is structured to work with Val.town and has the following components:\n\n- **Database Layer**: Uses Val.town's built-in SQLite database\n- **AI Integration**: Leverages Anthropic's Claude API for analysis\n- **Discord Integration**:\n - Bot token for fetching messages\n - Slash commands for user interactions\n - Webhook endpoint for Discord interactions\n- **API Endpoints**: Various endpoints for manual operations and automation\n\n## Setup\n\n### Environment Variables\n\nTo run this project on Val.town, you need to set up the following environment variables:\n\n- `DISCORD_TOKEN`: Your Discord bot token\n- `DISCORD_CLIENT_ID`: Your Discord application ID\n- `DISCORD_PUBLIC_KEY`: Public key for verifying Discord interactions\n- `DISCORD_USER_ID`: Your Discord user ID\n- `SPOUSE_USER_ID`: Your partner's Discord user ID\n- `ANTHROPIC_API_KEY`: Your Anthropic API key\n\n### Discord Bot Setup\n\n1. Create a Discord application: https://discord.com/developers/applications\n2. Add a bot to your application\n3. Set the required permissions:\n - Read Messages/View Channels\n - Read Message History\n - Send Messages\n4. Enable the Message Content Intent in the Bot section\n5. Use the `OAuth2 URL Generator` to create an invite link with the `bot` scope\n6. Add the bot to a server where both you and your partner are members\n\n### Val.town Deployment\n\n1. Create a new Val on Val.town\n2. Copy the contents of the backend directory\n3. Set up the required environment variables\n4. Create a cron schedule for periodically fetching messages and processing links\n\n## Usage\n\n### API Endpoints\n\n- `GET /health`: Check if the service is running\n- `POST /api/interactions`: Discord interactions endpoint\n- `POST /api/fetch-dms`: Manually trigger DM fetching\n- `POST /api/process-links`: Manually trigger link processing\n- `POST /api/query`: Process a query about your messages\n- `POST /api/commands/search`: Endpoint for search commands\n- `POST /api/register-commands`: Register Discord slash commands\n- `GET /api/cron/fetch-dms`: Cron endpoint for fetching DMs\n- `GET /api/cron/process-links`: Cron endpoint for processing links\n\n### Discord Commands\n\nThe bot provides several slash commands:\n\n- `/search [query]`: Search your conversation history\n- `/links [category]`: Find links shared in conversations, optionally filtered by category\n- `/categories`: List all available link categories\n- `/analyze [query]`: Perform a deeper analysis of your conversations\n\n## Development\n\n### Local Development\n\n1. Clone the repository\n2. Create a `.env` file with the required environment variables\n3. Install dependencies with `deno cache --reload index.ts`\n4. Run locally with `deno run --allow-net --allow-env index.ts`\n\n### Frontend\n\nA basic frontend is provided in the `frontend` directory. For now, we're focusing on the backend functionality, but the frontend can be extended to provide a web interface for searching and visualizing conversation data.\n\n## Val.town Specific Considerations\n\n- The database uses Val.town's built-in SQLite database\n- The code uses Deno and ESM modules compatible with Val.town\n- HTTP endpoints are designed to work with Val.town's serverless model\n- Discord verification is implemented according to Discord's security requirements\n\n## Credits\n\nThis project was ported from a Node.js implementation to work specifically with Val.town's environment.",[],[151,320],[151],{"_107":329,"_100":330,"_152":331,"_154":-5,"_155":139,"_156":332,"_158":120,"_109":333,"_104":31,"_160":278,"_162":334,"_163":27,"_164":-5,"_165":335,"_103":336},"7d081028-2710-11f0-b449-569c3dd06744",".cursorrules","7d0810dc-2710-11f0-b449-569c3dd06744",30,["D",1746161221692],"You are an advanced assistant specialized in generating Val Town code.\n\n## Core Guidelines\n\n- Ask clarifying questions when requirements are ambiguous\n- Provide complete, functional solutions rather than skeleton implementations\n- Test your logic against edge cases before presenting the final solution\n- Ensure all code follows Val Town's specific platform requirements\n- If a section of code that you're working on is getting too complex, consider refactoring it into subcomponents\n\n## Code Standards\n\n- Generate code in TypeScript or TSX\n- Add appropriate TypeScript types and interfaces for all data structures\n- Prefer official SDKs or libraries than writing API calls directly\n- Ask the user to supply API or library documentation if you are at all unsure about it\n- **Never bake in secrets into the code** - always use environment variables\n- Include comments explaining complex logic (avoid commenting obvious operations)\n- Follow modern ES6+ conventions and functional programming practices if possible\n\n## Types of triggers\n\n### 1. HTTP Trigger\n\n- Create web APIs and endpoints\n- Handle HTTP requests and responses\n- Example structure:\n\n```ts\nexport default async function (req: Request) {\n return new Response(\"Hello World\");\n}\n```\n\nFiles that are HTTP triggers have http in their name like `foobar.http.tsx`\n\n### 2. Cron Triggers\n\n- Run on a schedule\n- Use cron expressions for timing\n- Example structure:\n\n```ts\nexport default async function () {\n // Scheduled task code\n}\n```\n\nFiles that are Cron triggers have cron in their name like `foobar.cron.tsx`\n\n### 3. Email Triggers\n\n- Process incoming emails\n- Handle email-based workflows\n- Example structure:\n\n```ts\nexport default async function (email: Email) {\n // Process email\n}\n```\n\nFiles that are Email triggers have email in their name like `foobar.email.tsx`\n\n\n## Val Town Standard Libraries\n\nVal Town provides several hosted services and utility functions.\n\n### Blob Storage\n\n```ts\nimport { blob } from \"https://esm.town/v/std/blob\";\nawait blob.setJSON(\"myKey\", { hello: \"world\" });\nlet blobDemo = await blob.getJSON(\"myKey\");\nlet appKeys = await blob.list(\"app_\");\nawait blob.delete(\"myKey\");\n```\n\n### SQLite\n\n```ts\nimport { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nconst TABLE_NAME = 'todo_app_users_2';\n// Create table - do this before usage and change table name when modifying schema\nawait sqlite.execute(`CREATE TABLE IF NOT EXISTS ${TABLE_NAME} (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n name TEXT NOT NULL\n)`);\n// Query data\nconst result = await sqlite.execute(`SELECT * FROM ${TABLE_NAME} WHERE id = ?`, [1]);\n```\n\nNote: When changing a SQLite table's schema, change the table's name (e.g., add _2 or _3) to create a fresh table.\n\n### OpenAI\n\n```ts\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nconst openai = new OpenAI();\nconst completion = await openai.chat.completions.create({\n messages: [\n { role: \"user\", content: \"Say hello in a creative way\" },\n ],\n model: \"gpt-4o-mini\",\n max_tokens: 30,\n});\n```\n\n### Email\n\n```ts\nimport { email } from \"https://esm.town/v/std/email\";\n// By default emails the owner of the val\nawait email({ \n subject: \"Hi\", \n text: \"Hi\", \n html: \"

Hi

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

`\n- **Storage:** DO NOT use the Deno KV module for storage\n- **Browser APIs:** DO NOT use the `alert()`, `prompt()`, or `confirm()` methods\n- **Weather Data:** Use open-meteo for weather data (doesn't require API keys) unless otherwise specified\n- **View Source:** Add a view source link by importing & using `import.meta.url.replace(\"ems.sh\", \"val.town)\"` (or passing this data to the client) and include `target=\"_top\"` attribute\n- **Error Debugging:** Add `` to HTML to capture client-side errors\n- **Error Handling:** Only use try...catch when there's a clear local resolution; Avoid catches that merely log or return 500s. Let errors bubble up with full context\n- **Environment Variables:** Use `Deno.env.get('keyname')` when you need to, but generally prefer APIs that don't require keys\n- **Imports:** Use `https://esm.sh` for npm and Deno dependencies to ensure compatibility on server and browser\n- **Storage Strategy:** Only use backend storage if explicitly required; prefer simple static client-side sites\n- **React Configuration:** When using React libraries, pin versions with `?deps=react@18.2.0,react-dom@18.2.0` and start the file with `/** @jsxImportSource https://esm.sh/react@18.2.0 */`\n- Ensure all React dependencies and sub-dependencies are pinned to the same version\n- **Styling:** Default to using TailwindCSS via `` unless otherwise specified\n\n## Project Structure and Design Patterns\n\n### Recommended Directory Structure\n```\n├── backend/\n│ ├── database/\n│ │ ├── migrations.ts # Schema definitions\n│ │ ├── queries.ts # DB query functions\n│ │ └── README.md\n│ └── routes/ # Route modules\n│ ├── [route].ts\n│ └── static.ts # Static file serving\n│ ├── index.ts # Main entry point\n│ └── README.md\n├── frontend/\n│ ├── components/\n│ │ ├── App.tsx\n│ │ └── [Component].tsx\n│ ├── favicon.svg\n│ ├── index.html # Main HTML template\n│ ├── index.tsx # Frontend JS entry point\n│ ├── README.md\n│ └── style.css\n├── README.md\n└── shared/\n ├── README.md\n └── utils.ts # Shared types and functions\n```\n\n### Backend (Hono) Best Practices\n\n- Hono is the recommended API framework\n- Main entry point should be `backend/index.ts`\n- **Static asset serving:** Use the utility functions to read and serve project files:\n ```ts\n import { readFile, serveFile } from \"https://esm.town/v/std/utils@85-main/index.ts\";\n \n // serve all files in frontend/ and shared/\n app.get(\"/frontend/*\", c => serveFile(c.req.path, import.meta.url));\n app.get(\"/shared/*\", c => serveFile(c.req.path, import.meta.url));\n \n // For index.html, often you'll want to bootstrap with initial data\n app.get(\"/\", async c => {\n let html = await readFile(\"/frontend/index.html\", import.meta.url);\n \n // Inject data to avoid extra round-trips\n const initialData = await fetchInitialData();\n const dataScript = ``;\n \n html = html.replace(\"\", `${dataScript}`);\n return c.html(html);\n });\n ```\n- Create RESTful API routes for CRUD operations\n- Always include this snippet at the top-level Hono app to re-throwing errors to see full stack traces:\n ```ts\n // Unwrap Hono errors to see original error details\n app.onError((err, c) => {\n throw err;\n });\n ```\n\n### Database Patterns\n- Run migrations on startup or comment out for performance\n- Change table names when modifying schemas rather than altering\n- Export clear query functions with proper TypeScript typing\n\n## Common Gotchas and Solutions\n\n1. **Environment Limitations:** \n - Val Town runs on Deno in a serverless context, not Node.js\n - Code in `shared/` must work in both frontend and backend environments\n - Cannot use `Deno` keyword in shared code\n - Use `https://esm.sh` for imports that work in both environments\n\n2. **SQLite Peculiarities:**\n - Limited support for ALTER TABLE operations\n - Create new tables with updated schemas and copy data when needed\n - Always run table creation before querying\n\n3. **React Configuration:**\n - All React dependencies must be pinned to 18.2.0\n - Always include `@jsxImportSource https://esm.sh/react@18.2.0` at the top of React files\n - Rendering issues often come from mismatched React versions\n\n4. **File Handling:**\n - Val Town only supports text files, not binary\n - Use the provided utilities to read files across branches and forks\n - For files in the project, use `readFile` helpers\n\n5. **API Design:**\n - `fetch` handler is the entry point for HTTP vals\n - Run the Hono app with `export default app.fetch // This is the entry point for HTTP vals`\n\n",[],[330],{"_107":338,"_100":339,"_152":340,"_154":-5,"_155":139,"_156":341,"_158":120,"_109":342,"_104":31,"_160":278,"_162":343,"_163":27,"_164":-5,"_165":344,"_103":345},"7d0db64a-2710-11f0-b504-569c3dd06744",".env.example","7d0db7f8-2710-11f0-b504-569c3dd06744",31,["D",1746161221791],"# Discord API credentials\nDISCORD_TOKEN=your_discord_token_here\nDISCORD_CLIENT_ID=your_client_id_here\nDISCORD_USER_ID=your_user_id_here\nSPOUSE_USER_ID=spouse_user_id_here\n\n# OpenAI API key (for AI-powered querying)\nOPENAI_API_KEY=your_openai_api_key_here\n\n# Val.town settings\nVT_CRON_SCHEDULE=\"0 */6 * * *\" # Every 6 hours",[],[339],{"_107":347,"_100":348,"_152":349,"_154":-5,"_155":139,"_156":350,"_158":120,"_109":351,"_104":31,"_160":278,"_162":352,"_163":27,"_164":-5,"_165":353,"_103":354},"7d14db28-2710-11f0-89d1-569c3dd06744",".vtignore","7d14dbdc-2710-11f0-89d1-569c3dd06744",33,["D",1746161221852],".git\n.vscode\n.DS_Store\nnode_modules\nvendor",[],[348],{"_107":356,"_100":357,"_152":358,"_154":-5,"_155":139,"_156":359,"_158":120,"_109":360,"_104":31,"_160":278,"_162":361,"_163":27,"_164":-5,"_165":362,"_103":363},"7cb3e872-2710-11f0-bc07-569c3dd06744","DISCORD_BOT_SETUP.md","7cb3e930-2710-11f0-bc07-569c3dd06744",8,["D",1746161221312],"# Step-by-Step Discord Bot Setup Guide\n\nThis guide will walk you through creating a Discord bot and setting it up to work with the Discord DM Analyzer.\n\n## 1. Create a Discord Application\n\n1. Go to the [Discord Developer Portal](https://discord.com/developers/applications)\n2. Click the \"New Application\" button in the top-right corner\n3. Enter a name for your application (e.g., \"DM Analyzer\")\n4. Accept the terms and click \"Create\"\n\n## 2. Set Up the Bot\n\n1. In the left sidebar, click on \"Bot\"\n2. Click the \"Add Bot\" button on the right\n3. Confirm by clicking \"Yes, do it!\"\n4. Under the bot's username, you'll see a \"Reset Token\" button\n5. Click \"Reset Token\" and confirm\n6. Copy the token that appears - this is your `DISCORD_TOKEN`\n7. Under \"Privileged Gateway Intents\", enable:\n - MESSAGE CONTENT INTENT\n - SERVER MEMBERS INTENT\n8. Scroll down and click \"Save Changes\"\n\n## 3. Get Your Application ID\n\n1. In the left sidebar, click on \"General Information\"\n2. Under \"Application ID\", click the \"Copy\" button\n3. This is your `DISCORD_CLIENT_ID`\n\n## 4. Get Your User ID\n\n1. Open Discord\n2. Go to User Settings (gear icon next to your name)\n3. Select \"Advanced\" in the left sidebar\n4. Enable \"Developer Mode\"\n5. Close User Settings\n6. Right-click on your username anywhere in Discord\n7. Select \"Copy ID\"\n8. This is your `DISCORD_USER_ID`\n\n## 5. Get Your Spouse's User ID\n\n1. Right-click on your spouse's username in Discord\n2. Select \"Copy ID\"\n3. This is your `SPOUSE_USER_ID`\n\n## 6. Update Environment Variables\n\n1. In your project directory, create a `.env` file if you haven't already\n2. Add the following variables:\n ```\n DISCORD_TOKEN=your_discord_token_here\n DISCORD_CLIENT_ID=your_client_id_here\n DISCORD_USER_ID=your_user_id_here\n SPOUSE_USER_ID=spouse_user_id_here\n ```\n\n## 7. Get OpenAI API Key\n\n1. Go to [OpenAI's website](https://platform.openai.com/)\n2. Sign up or log in\n3. Navigate to the API keys section\n4. Create a new API key\n5. Copy the key\n6. Add it to your `.env` file:\n ```\n OPENAI_API_KEY=your_openai_api_key_here\n ```\n\n## 8. Register Bot Commands\n\n1. In your project directory, run:\n ```\n node discord-commands.js\n ```\n2. You should see \"Successfully registered application commands.\"\n\n## 9. Invite the Bot to Your Server\n\n1. In the Discord Developer Portal, click on \"OAuth2\" in the left sidebar\n2. Click on \"URL Generator\"\n3. Under \"Scopes\", select:\n - bot\n - applications.commands\n4. Under \"Bot Permissions\", select:\n - Read Messages/View Channels\n - Send Messages\n - Read Message History\n5. Copy the generated URL at the bottom\n6. Paste the URL in your browser\n7. Select a server where you want to add the bot\n8. Click \"Authorize\"\n9. Complete the CAPTCHA if prompted\n\n## 10. Deploy to Val.town (Optional)\n\n1. Install the Val.town CLI:\n ```\n npm install -g val-town-cli\n ```\n2. Authenticate with Val.town:\n ```\n vt login\n ```\n3. Deploy the application:\n ```\n node val-town-deploy.js\n ```\n4. Set up environment variables in Val.town:\n - Go to the Val.town dashboard\n - Find your deployed functions\n - Add the same environment variables from your `.env` file\n5. Set up a scheduled task for periodic DM fetching:\n - Create a new scheduled task in Val.town\n - Use the `discordDMBotCron` function\n - Choose a schedule (e.g., every 6 hours)\n\n## 11. Test Your Bot\n\n1. Start the local server:\n ```\n node index.js\n ```\n2. In Discord, use one of the slash commands:\n - `/search What did we say about the IKEA table?`\n - `/links sofa`\n - `/categories`\n - `/analyze Summarize our furniture discussions`\n\n3. You can also manually trigger a DM fetch:\n ```\n node val-town-cron.js\n ```\n\n## Troubleshooting\n\n### Bot not responding to commands\n- Make sure the bot is running (`node index.js`)\n- Check if commands were registered successfully\n- Verify that the bot has proper permissions\n\n### Cannot fetch DMs\n- Ensure both you and your spouse have DMed each other at least once\n- Verify that all tokens and IDs in the `.env` file are correct\n- Check Discord developer portal for any token or permission issues\n\n### Val.town deployment issues\n- Make sure you're logged in to Val.town CLI\n- Check for any errors in the deployment logs\n- Verify that environment variables are set correctly in Val.town\n\n## Next Steps\n\n1. Customize the bot's responses by editing the API endpoints in `api-server.js`\n2. Add more categories for link classification in `database.js`\n3. Enhance the AI queries by modifying prompts in `query-service.js`\n4. Create a web interface that connects to your API endpoints",[],[357],{"_107":365,"_100":320,"_152":366,"_154":-5,"_155":139,"_156":367,"_158":120,"_109":368,"_104":31,"_160":278,"_162":369,"_163":27,"_164":-5,"_165":370,"_103":371},"7cfa20da-2710-11f0-9826-569c3dd06744","7cfa2166-2710-11f0-9826-569c3dd06744",27,["D",1746161221708],"# Discord DM Analyzer\n\nA Discord bot that analyzes direct messages between you and your partner, allowing you to search and query conversation history, track shared links, and more. Perfect for finding those furniture links you shared months ago!\n\n## Features\n\n- **Message History Storage**: Securely stores your DM history in a SQLite database\n- **Link Detection & Categorization**: Automatically detects and categorizes links shared in conversations\n- **AI-Powered Querying**: Ask natural language questions about your conversation history\n- **Furniture Focus**: Special features for tracking furniture links and discussions\n- **Val.town Integration**: Runs on Val.town for easy deployment and scheduling\n- **Periodic Updates**: Automatically fetches new messages on a schedule\n\n## Use Cases\n\n- \"Show me all the sofas we discussed last month\"\n- \"What did we say about the IKEA table?\"\n- \"Find all the furniture links we shared since January\"\n- \"Summarize our discussion about the dining room\"\n\n## Setup Instructions\n\n### Prerequisites\n\n- Node.js 16+ and npm\n- Discord account with Bot token\n- OpenAI API key (for AI-powered queries)\n- Val.town account (for deployment)\n\n### Local Development Setup\n\n1. Clone this repository\n ```\n git clone \n cd discord-bot\n ```\n\n2. Install dependencies\n ```\n npm install\n ```\n\n3. Create a `.env` file based on `.env.example`\n ```\n cp .env.example .env\n ```\n\n4. Fill in the required environment variables in `.env`:\n - `DISCORD_TOKEN`: Your Discord bot token\n - `DISCORD_CLIENT_ID`: Your Discord application client ID\n - `DISCORD_USER_ID`: Your Discord user ID\n - `SPOUSE_USER_ID`: Your partner's Discord user ID\n - `OPENAI_API_KEY`: Your OpenAI API key\n\n5. Initialize the database\n ```\n node index.js\n ```\n\n6. Test the DM fetching process\n ```\n node val-town-cron.js\n ```\n\n### Val.town Deployment\n\n1. Install the Val.town CLI\n ```\n npm install -g val-town-cli\n ```\n\n2. Authenticate with Val.town\n ```\n vt login\n ```\n\n3. Deploy the application to Val.town\n ```\n node val-town-deploy.js\n ```\n\n4. Configure Val.town environment variables\n - Go to the Val.town dashboard\n - Add the same environment variables from your `.env` file\n\n5. Set up a scheduled task on Val.town for periodic DM fetching\n - Create a new scheduled task using the Val.town dashboard\n - Use the `discordDMBotCron` function\n - Set the schedule (e.g., every 6 hours)\n\n### Creating a Discord Bot\n\n1. Go to the [Discord Developer Portal](https://discord.com/developers/applications)\n2. Create a New Application\n3. Go to the \"Bot\" tab and create a bot\n4. Under \"Privileged Gateway Intents\", enable:\n - Message Content Intent\n - Server Members Intent\n5. Copy the bot token to your `.env` file\n6. Invite the bot to your server using the OAuth2 URL Generator\n - Select \"bot\" scope\n - Select permissions: Read Messages/View Channels, Read Message History\n7. Start a DM with your partner to create the DM channel the bot will monitor\n\n## API Endpoints\n\nThe application exposes the following HTTP endpoints:\n\n- `GET /health`: Health check endpoint\n- `POST /api/fetch-dms`: Manually trigger DM fetching\n- `POST /api/process-links`: Manually trigger link processing\n- `POST /api/query`: Submit a query about your message history\n - Body: `{ \"query\": \"your question here\", \"filters\": { optional filters } }`\n- `POST /api/commands/search`: Discord command endpoint for searching\n - Body: `{ \"query\": \"your search term\" }`\n\n## Discord Commands\n\nOnce set up, you can interact with the bot using the following commands:\n\n- `/search [query]`: Search your conversation history\n- `/links [category]`: Show links from a specific category\n- `/categories`: List all available link categories\n- `/analyze [query]`: Analyze your conversation with AI\n\n## Database Schema\n\nThe application uses a SQLite database with the following tables:\n\n- `messages`: Stores all DM messages\n- `attachments`: Stores message attachments\n- `embeds`: Stores message embeds\n- `links`: Stores extracted links with categories\n- `categories`: Stores link categories\n- `conversations`: Stores AI-generated conversation summaries\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n\n## Acknowledgements\n\n- Discord.js for Discord API integration\n- OpenAI for AI-powered querying\n- Val.town for hosting and scheduling\n- Better-SQLite3 for database management",[],[320],{"_107":373,"_100":374,"_152":375,"_154":-5,"_155":139,"_156":376,"_158":120,"_109":377,"_104":31,"_160":180,"_162":378,"_163":27,"_164":-5,"_165":379,"_103":380},"7d1c2090-2710-11f0-8927-569c3dd06744","anthropic-service.js","7d1c2158-2710-11f0-8927-569c3dd06744",35,["D",1746161221946],"import Anthropic from '@anthropic-ai/sdk';\nimport 'dotenv/config';\n\n// Initialize Anthropic client\nconst anthropic = new Anthropic({\n apiKey: process.env.ANTHROPIC_API_KEY,\n});\n\n/**\n * Process a message with Anthropic Claude\n * @param {string} prompt - The prompt to send to Claude\n * @param {Object} options - Additional options\n * @returns {Promise} - Claude's response\n */\nexport async function processWithClaude(prompt, options = {}) {\n try {\n const message = await anthropic.messages.create({\n model: options.model || 'claude-3-haiku-20240307',\n max_tokens: options.maxTokens || 1000,\n temperature: options.temperature || 0.7,\n system: options.systemPrompt || 'You are an assistant analyzing Discord conversations and helping users find information from their message history.',\n messages: [\n {\n role: 'user',\n content: prompt\n }\n ]\n });\n \n return message.content[0].text;\n } catch (error) {\n console.error('Error processing with Claude:', error);\n throw error;\n }\n}\n\n/**\n * Analyze conversation data with Claude\n * @param {Array} messages - Array of message objects\n * @param {string} query - The user's query\n * @returns {Promise} - Claude's analysis\n */\nexport async function analyzeConversation(messages, query) {\n try {\n // Format the messages for Claude\n const formattedMessages = messages.map(msg => {\n const author = msg.author_id === process.env.DISCORD_USER_ID ? 'You' : 'Partner';\n const date = new Date(parseInt(msg.timestamp)).toLocaleString();\n return `${author} (${date}): ${msg.content}`;\n }).join('\\n\\n');\n \n // Create the prompt\n const prompt = `\nI have a set of Discord direct messages between me and my partner. \nI want to analyze them to answer this question: \"${query}\"\n\nHere are the relevant messages:\n\n${formattedMessages}\n\nPlease analyze these messages to answer my question. Focus on providing concrete \ninformation and insights directly from the conversation. If there isn't enough \ninformation to answer the question, please say so.\n `;\n \n // Get Claude's response\n const response = await processWithClaude(prompt, {\n systemPrompt: `\nYou are an assistant that helps users analyze their private conversations with their partner.\nYour task is to carefully review the messages and provide thoughtful, nuanced analysis.\nFocus on finding concrete information and insights that directly address the user's query.\nBe concise but thorough, including specific quotes or references when relevant.\nRespect the privacy and sensitivity of personal conversations.\nIf you can't find enough information to answer a question, clearly acknowledge this limitation.\n `,\n maxTokens: 1500\n });\n \n return response;\n } catch (error) {\n console.error('Error analyzing conversation:', error);\n throw error;\n }\n}\n\n/**\n * Categorize content using Claude\n * @param {string} content - The content to categorize\n * @param {Array} categories - Available categories\n * @returns {Promise} - Suggested category\n */\nexport async function categorizeContent(content, categories = []) {\n try {\n const categoriesList = categories.length > 0 \n ? categories.join(', ') \n : 'sofa, chair, table, bed, dresser, desk, lamp, shelf, bookcase, rug, curtain, other';\n \n const prompt = `\nI need to categorize this content:\n\n\"${content}\"\n\nPlease categorize it into one of these categories: ${categoriesList}\n\nReturn only the category name, nothing else.\n `;\n \n const response = await processWithClaude(prompt, {\n systemPrompt: 'You are a content categorization assistant. Your task is to analyze the given content and assign it to the most appropriate category from the provided list. Return only the category name, with no additional text.',\n maxTokens: 50,\n temperature: 0.3\n });\n \n // Process the response to get just the category\n const category = response.trim().toLowerCase();\n \n // Verify it's a valid category\n const validCategories = categories.length > 0 \n ? categories \n : ['sofa', 'chair', 'table', 'bed', 'dresser', 'desk', 'lamp', 'shelf', 'bookcase', 'rug', 'curtain', 'other'];\n \n if (validCategories.includes(category)) {\n return category;\n } else {\n return 'other';\n }\n } catch (error) {\n console.error('Error categorizing content:', error);\n return 'other';\n }\n}\n\n/**\n * Summarize multiple messages with Claude\n * @param {Array} messages - Array of message objects\n * @param {string} topic - Optional topic to focus on\n * @returns {Promise

Key	Description
ANTHROPIC_API_KEY
DISCORD_CLIENT_ID
DISCORD_PUBLIC_KEY
DISCORD_TOKEN
DISCORD_USER_ID
SPOUSE_USER_ID
VT_CRON_SCHEDULE

Environment variables