[{"_1":2,"_172":-5,"_173":-5},"loaderData",{"_3":4,"_91":92},"root",{"_5":6,"_34":35,"_64":65},"ctx",{"_7":8,"_23":-5,"_24":25,"_26":27,"_28":20,"_29":20,"_30":20,"_31":32,"_33":20},"auth",{"_9":-5,"_10":-5,"_11":-5,"_12":-5,"_13":-5,"_14":-5,"_15":-5,"_16":-5,"_17":-5,"_18":-5,"_19":20,"_21":22},"sessionClaims","sessionId","sessionStatus","userId","actor","orgId","orgRole","orgSlug","orgPermissions","factorVerificationAge","isAuthenticated",false,"tokenType","session_token","user","preferencesKeybindings","default","preferencesTheme","light","preferencesFixers","preferencesCopilot","preferencesSelectionAi","notificationCount",0,"flagDenols","ENV",{"_36":37,"_38":39,"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59,"_60":61,"_62":63},"REACT_APP_SENTRY_DSN","https://586020d86f05489db63a554690acf1e9@o1398359.ingest.sentry.io/6724499","VAL_RUNTIME_NAME","prod","RENDER_GIT_COMMIT","86c278eb598060f33e9f047762b0f599ada2ba10","RENDER_GIT_BRANCH","main","CLERK_PUBLISHABLE_KEY","pk_live_Y2xlcmsudmFsLnRvd24k","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","API_URL","https://api.val.town","clerkState",{"_66":67},"__internal_clerk_state",{"_68":69,"_70":45,"_71":72,"_73":72,"_74":20,"_75":72,"_76":72,"_77":72,"_78":72,"_79":80,"_89":20,"_90":20},"__clerk_ssr_state",{"_21":22,"_9":-5,"_10":-5,"_11":-5,"_12":-5,"_13":-5,"_14":-5,"_15":-5,"_16":-5,"_17":-5,"_18":-5,"_19":20},"__publishableKey","__proxyUrl","","__domain","__isSatellite","__signInUrl","__signUpUrl","__afterSignInUrl","__afterSignUpUrl","__clerk_debug",{"_81":20,"_19":20,"_82":72,"_83":84,"_85":72,"_86":45,"_87":20,"_88":72},"isSignedIn","proxyUrl","reason","session-token-and-uat-missing","message","publishableKey","isSatellite","domain","__telemetryDisabled","__telemetryDebug","routes/embed.x.$handle.$name.$",{"_93":94,"_95":96,"_97":98,"_99":100,"_146":147,"_169":170,"_171":-5},"handle","zee1205","name","untitled-1082","path","backend/database/queries.ts","project",{"_101":102,"_95":96,"_103":104,"_105":-5,"_106":20,"_107":108,"_109":-5,"_110":-5,"_12":111,"_112":-5,"_113":20,"_114":-5,"_115":32,"_14":116,"_117":118,"_119":118,"_120":121,"_122":121,"_123":121,"_124":118,"_125":118,"_23":126,"_129":130,"_132":133,"_145":20},"id","20e65120-409b-11f0-ad1f-76b3cceeab13","createdAt",["D",1748969546453],"deletedAt","legacyEnvVars","privacy","public","description","imageUrl","8cc83f10-406a-11f0-bfbe-7da0210b7c9d","pinnedFileId","pinPreview","screenshotVersion","likeCount","35eb0c04-e34c-4c24-9b96-4124b52422e1","userPerm","r","valPerm","blobPerm","rw","emailPerm","sqlitePerm","projectPerm","telemetryPerm",{"_93":94,"_127":128},"profileImageUrl","https://img.clerk.com/eyJ0eXBlIjoicHJveHkiLCJzcmMiOiJodHRwczovL2ltYWdlcy5jbGVyay5kZXYvb2F1dGhfZ2l0aHViL2ltZ18yeHpsRDdFZkFKUmZHMGtWNDl6bVR6Q3NHaG4ifQ","org",{"_93":94,"_131":128},"avatar","branch",{"_134":135,"_95":43,"_101":136,"_137":102,"_138":-5,"_139":-5,"_140":-5,"_141":-5,"_142":-5,"_143":96,"_144":94},"version",26,"20e6bad4-409b-11f0-ad1f-76b3cceeab13","projectId","forkedBranchId","forkedBranchVersion","forkedProjectPrivacy","forkedProjectId","forkedProjectOrgId","projectName","userHandle","isOwned","file",{"_101":148,"_95":149,"_150":151,"_152":153,"_154":136,"_155":156,"_157":158,"_103":159,"_134":32,"_160":161,"_162":163,"_164":20,"_165":-5,"_97":166},"7e021cea-409b-11f0-85a8-76b3cceeab13","queries.ts","fileId","7e021dbc-409b-11f0-85a8-76b3cceeab13","parentId","b23a0e26-c01c-4fed-b68a-045de5b8adca","branchId","branchVersion",6,"latest",true,["D",1748969702623],"fileType","script","content","import { sqlite } from \"https://esm.town/v/stevekrouse/sqlite\";\nimport { TABLES } from \"./migrations.ts\";\n\nexport interface MedicalContent {\n id: number;\n content: string;\n embedding_vector?: number[];\n chapter: string;\n specialty: string;\n page_reference: string;\n keywords: string[];\n content_type: string;\n confidence_score: number;\n created_at: string;\n}\n\nexport interface Conversation {\n id: string;\n user_id: string;\n title: string;\n messages: any[];\n specialty_filter?: string;\n total_messages: number;\n created_at: string;\n updated_at: string;\n is_archived: boolean;\n}\n\nexport interface UserPreferences {\n user_id: string;\n display_name?: string;\n preferred_specialties: string[];\n ui_theme: 'light' | 'dark';\n streaming_enabled: boolean;\n offline_mode: boolean;\n disclaimer_acknowledged: boolean;\n}\n\n// Vector similarity search using cosine similarity\nexport async function searchMedicalContent(\n queryEmbedding: number[],\n limit: number = 5,\n specialtyFilter?: string,\n chapterFilter?: string\n): Promise {\n try {\n let query = `\n SELECT id, content, embedding_vector, chapter, specialty, \n page_reference, keywords, content_type, confidence_score, created_at\n FROM ${TABLES.MEDICAL_CONTENT}\n WHERE embedding_vector IS NOT NULL\n `;\n \n const params: any[] = [];\n \n if (specialtyFilter) {\n query += ` AND specialty = ?`;\n params.push(specialtyFilter);\n }\n \n if (chapterFilter) {\n query += ` AND chapter = ?`;\n params.push(chapterFilter);\n }\n \n query += ` ORDER BY created_at DESC LIMIT ?`;\n params.push(limit * 3); // Get more results for similarity calculation\n \n const results = await sqlite.execute(query, params);\n \n // Calculate cosine similarity for each result\n const scoredResults = results.map((row: any) => {\n const embedding = row.embedding_vector ? JSON.parse(row.embedding_vector) : null;\n const similarity = embedding ? cosineSimilarity(queryEmbedding, embedding) : 0;\n \n return {\n ...row,\n embedding_vector: embedding,\n keywords: row.keywords ? JSON.parse(row.keywords) : [],\n similarity_score: similarity\n };\n });\n \n // Sort by similarity and return top results\n return scoredResults\n .sort((a, b) => b.similarity_score - a.similarity_score)\n .slice(0, limit);\n \n } catch (error) {\n console.error(\"Error searching medical content:\", error);\n return [];\n }\n}\n\n// Fallback text search when embeddings are not available\nexport async function textSearchMedicalContent(\n query: string,\n limit: number = 5,\n specialtyFilter?: string\n): Promise {\n try {\n const searchTerms = query.toLowerCase().split(' ').filter(term => term.length > 2);\n \n let sqlQuery = `\n SELECT id, content, chapter, specialty, page_reference, \n keywords, content_type, confidence_score, created_at\n FROM ${TABLES.MEDICAL_CONTENT}\n WHERE (\n `;\n \n const params: any[] = [];\n const conditions: string[] = [];\n \n searchTerms.forEach(term => {\n conditions.push(`LOWER(content) LIKE ?`);\n conditions.push(`LOWER(keywords) LIKE ?`);\n params.push(`%${term}%`, `%${term}%`);\n });\n \n sqlQuery += conditions.join(' OR ') + ')';\n \n if (specialtyFilter) {\n sqlQuery += ` AND specialty = ?`;\n params.push(specialtyFilter);\n }\n \n sqlQuery += ` ORDER BY confidence_score DESC LIMIT ?`;\n params.push(limit);\n \n const results = await sqlite.execute(sqlQuery, params);\n \n return results.map((row: any) => ({\n ...row,\n keywords: row.keywords ? JSON.parse(row.keywords) : []\n }));\n \n } catch (error) {\n console.error(\"Error in text search:\", error);\n return [];\n }\n}\n\n// Conversation management\nexport async function saveConversation(conversation: Partial): Promise {\n try {\n const id = conversation.id || generateConversationId();\n \n await sqlite.execute(`\n INSERT OR REPLACE INTO ${TABLES.CONVERSATIONS}\n (id, user_id, title, messages, specialty_filter, total_messages, updated_at)\n VALUES (?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)\n `, [\n id,\n conversation.user_id || 'anonymous',\n conversation.title || 'New Conversation',\n JSON.stringify(conversation.messages || []),\n conversation.specialty_filter || null,\n conversation.messages?.length || 0\n ]);\n \n return id;\n } catch (error) {\n console.error(\"Error saving conversation:\", error);\n throw error;\n }\n}\n\nexport async function getConversation(id: string): Promise {\n try {\n const results = await sqlite.execute(\n `SELECT * FROM ${TABLES.CONVERSATIONS} WHERE id = ?`,\n [id]\n );\n \n if (results.length === 0) return null;\n \n const row = results[0];\n return {\n ...row,\n messages: JSON.parse(row.messages || '[]')\n };\n } catch (error) {\n console.error(\"Error getting conversation:\", error);\n return null;\n }\n}\n\nexport async function getUserConversations(\n userId: string,\n limit: number = 20\n): Promise {\n try {\n const results = await sqlite.execute(`\n SELECT * FROM ${TABLES.CONVERSATIONS}\n WHERE user_id = ? AND is_archived = FALSE\n ORDER BY updated_at DESC\n LIMIT ?\n `, [userId, limit]);\n \n return results.map((row: any) => ({\n ...row,\n messages: JSON.parse(row.messages || '[]')\n }));\n } catch (error) {\n console.error(\"Error getting user conversations:\", error);\n return [];\n }\n}\n\n// User preferences\nexport async function saveUserPreferences(preferences: UserPreferences): Promise {\n try {\n await sqlite.execute(`\n INSERT OR REPLACE INTO ${TABLES.USER_PREFERENCES}\n (user_id, display_name, preferred_specialties, ui_theme, \n streaming_enabled, offline_mode, disclaimer_acknowledged, updated_at)\n VALUES (?, ?, ?, ?, ?, ?, ?, CURRENT_TIMESTAMP)\n `, [\n preferences.user_id,\n preferences.display_name || null,\n JSON.stringify(preferences.preferred_specialties),\n preferences.ui_theme,\n preferences.streaming_enabled,\n preferences.offline_mode,\n preferences.disclaimer_acknowledged\n ]);\n } catch (error) {\n console.error(\"Error saving user preferences:\", error);\n throw error;\n }\n}\n\nexport async function getUserPreferences(userId: string): Promise {\n try {\n const results = await sqlite.execute(\n `SELECT * FROM ${TABLES.USER_PREFERENCES} WHERE user_id = ?`,\n [userId]\n );\n \n if (results.length === 0) return null;\n \n const row = results[0];\n return {\n ...row,\n preferred_specialties: JSON.parse(row.preferred_specialties || '[]')\n };\n } catch (error) {\n console.error(\"Error getting user preferences:\", error);\n return null;\n }\n}\n\n// Embedding cache management\nexport async function cacheEmbedding(\n queryText: string,\n embedding: number[]\n): Promise {\n try {\n const queryHash = await hashString(queryText);\n \n await sqlite.execute(`\n INSERT OR REPLACE INTO ${TABLES.EMBEDDINGS_CACHE}\n (query_hash, query_text, embedding_vector, last_accessed, access_count)\n VALUES (?, ?, ?, CURRENT_TIMESTAMP, \n COALESCE((SELECT access_count FROM ${TABLES.EMBEDDINGS_CACHE} WHERE query_hash = ?), 0) + 1)\n `, [queryHash, queryText, JSON.stringify(embedding), queryHash]);\n } catch (error) {\n console.error(\"Error caching embedding:\", error);\n }\n}\n\nexport async function getCachedEmbedding(queryText: string): Promise {\n try {\n const queryHash = await hashString(queryText);\n \n const results = await sqlite.execute(\n `SELECT embedding_vector FROM ${TABLES.EMBEDDINGS_CACHE} WHERE query_hash = ?`,\n [queryHash]\n );\n \n if (results.length === 0) return null;\n \n // Update access count\n await sqlite.execute(\n `UPDATE ${TABLES.EMBEDDINGS_CACHE} \n SET access_count = access_count + 1, last_accessed = CURRENT_TIMESTAMP\n WHERE query_hash = ?`,\n [queryHash]\n );\n \n return JSON.parse(results[0].embedding_vector);\n } catch (error) {\n console.error(\"Error getting cached embedding:\", error);\n return null;\n }\n}\n\n// Utility functions\nfunction cosineSimilarity(a: number[], b: number[]): number {\n if (a.length !== b.length) return 0;\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 if (normA === 0 || normB === 0) return 0;\n \n return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));\n}\n\nfunction generateConversationId(): string {\n return 'conv_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9);\n}\n\nasync function hashString(str: string): Promise {\n const encoder = new TextEncoder();\n const data = encoder.encode(str);\n const hashBuffer = await crypto.subtle.digest('SHA-256', data);\n const hashArray = Array.from(new Uint8Array(hashBuffer));\n return hashArray.map(b => b.toString(16).padStart(2, '0')).join('');\n}","deleted","label",[167,168,149],"backend","database","permalink","https://www.val.town/x/zee1205/untitled-1082/code/backend/database/queries.ts","extension","actionData","errors"]