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{ blob } from \"https://esm.town/v/std/blob\";\nimport type { SOARESystem, SystemComponent, SOAREEvent } from \"../../../shared/types/core.ts\";\n\ninterface VectorEntry {\n id: string;\n content: string;\n embedding: number[];\n metadata: {\n type: 'system' | 'component' | 'event' | 'knowledge' | 'pattern';\n timestamp: Date;\n tags: string[];\n relevance?: number;\n };\n}\n\ninterface SearchResult {\n entry: VectorEntry;\n similarity: number;\n}\n\nexport class SOAREMemory {\n private cache: Map = new Map();\n private initialized = false;\n\n async initialize(): Promise {\n if (this.initialized) return;\n \n try {\n // Load existing vectors from blob storage\n const existingVectors = await blob.getJSON(\"soare_vectors\") as VectorEntry[] || [];\n for (const vector of existingVectors) {\n this.cache.set(vector.id, vector);\n }\n this.initialized = true;\n } catch (error) {\n console.warn(\"Could not load existing vectors, starting fresh:\", error);\n this.initialized = true;\n }\n }\n\n async store(content: string, metadata: VectorEntry['metadata']): Promise {\n await this.initialize();\n \n const id = `vec_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;\n const embedding = await this.generateEmbedding(content);\n \n const entry: VectorEntry = {\n id,\n content,\n embedding,\n metadata: {\n ...metadata,\n timestamp: new Date()\n }\n };\n \n this.cache.set(id, entry);\n await this.persistVectors();\n \n return id;\n }\n\n async storeSystem(system: SOARESystem): Promise {\n const content = this.systemToText(system);\n return await this.store(content, {\n type: 'system',\n timestamp: new Date(),\n tags: ['system', system.status, ...system.goals.map(g => g.description.split(' ')[0])]\n });\n }\n\n async storeComponent(component: SystemComponent, systemId: string): Promise {\n const content = this.componentToText(component);\n return await this.store(content, {\n type: 'component',\n timestamp: new Date(),\n tags: ['component', component.type, component.status, systemId]\n });\n }\n\n async storeEvent(event: SOAREEvent): Promise {\n const content = `Event: ${event.type} from ${event.source}\\nData: ${JSON.stringify(event.data)}`;\n return await this.store(content, {\n type: 'event',\n timestamp: event.timestamp,\n tags: ['event', event.type, event.source]\n });\n }\n\n async storeKnowledge(knowledge: string, tags: string[]): Promise {\n return await this.store(knowledge, {\n type: 'knowledge',\n timestamp: new Date(),\n tags: ['knowledge', ...tags]\n });\n }\n\n async search(query: string, options: {\n type?: VectorEntry['metadata']['type'];\n tags?: string[];\n limit?: number;\n threshold?: number;\n } = {}): Promise {\n await this.initialize();\n \n const queryEmbedding = await this.generateEmbedding(query);\n const results: SearchResult[] = [];\n \n for (const entry of this.cache.values()) {\n // Filter by type if specified\n if (options.type && entry.metadata.type !== options.type) continue;\n \n // Filter by tags if specified\n if (options.tags && !options.tags.some(tag => entry.metadata.tags.includes(tag))) continue;\n \n const similarity = this.cosineSimilarity(queryEmbedding, entry.embedding);\n \n // Apply threshold filter\n if (similarity >= (options.threshold || 0.1)) {\n results.push({ entry, similarity });\n }\n }\n \n // Sort by similarity and limit results\n results.sort((a, b) => b.similarity - a.similarity);\n return results.slice(0, options.limit || 10);\n }\n\n async findSimilarSystems(system: SOARESystem, limit = 5): Promise {\n const query = this.systemToText(system);\n return await this.search(query, {\n type: 'system',\n limit,\n threshold: 0.3\n });\n }\n\n async findRelevantKnowledge(context: string, tags: string[] = []): Promise {\n return await this.search(context, {\n type: 'knowledge',\n tags,\n limit: 5,\n threshold: 0.2\n });\n }\n\n async findPatterns(eventType: string): Promise {\n return await this.search(`pattern analysis for ${eventType}`, {\n type: 'pattern',\n limit: 3,\n threshold: 0.4\n });\n }\n\n async getRecentEvents(hours = 24): Promise {\n await this.initialize();\n \n const cutoff = new Date(Date.now() - hours * 60 * 60 * 1000);\n const events: VectorEntry[] = [];\n \n for (const entry of this.cache.values()) {\n if (entry.metadata.type === 'event' && entry.metadata.timestamp >= cutoff) {\n events.push(entry);\n }\n }\n \n return events.sort((a, b) => b.metadata.timestamp.getTime() - a.metadata.timestamp.getTime());\n }\n\n async analyzePatterns(systemId?: string): Promise<{\n commonPatterns: string[];\n anomalies: string[];\n trends: string[];\n }> {\n await this.initialize();\n \n const events = systemId \n ? Array.from(this.cache.values()).filter(e => \n e.metadata.type === 'event' && e.metadata.tags.includes(systemId)\n )\n : Array.from(this.cache.values()).filter(e => e.metadata.type === 'event');\n \n // Simple pattern analysis - could be enhanced with ML\n const eventTypes = events.map(e => e.metadata.tags[1]).filter(Boolean);\n const typeCounts = eventTypes.reduce((acc, type) => {\n acc[type] = (acc[type] || 0) + 1;\n return acc;\n }, {} as Record);\n \n const commonPatterns = Object.entries(typeCounts)\n .filter(([_, count]) => count > 2)\n .map(([type, count]) => `${type} occurs frequently (${count} times)`);\n \n const anomalies = Object.entries(typeCounts)\n .filter(([_, count]) => count === 1)\n .map(([type]) => `${type} is an unusual event`);\n \n const trends = this.analyzeTrends(events);\n \n return { commonPatterns, anomalies, trends };\n }\n\n private async generateEmbedding(text: string): Promise {\n // Simplified embedding generation using text characteristics\n // In a real implementation, this would use a proper embedding model\n const words = text.toLowerCase().split(/\\s+/);\n const embedding = new Array(384).fill(0); // Standard embedding size\n \n // Simple hash-based embedding for demonstration\n for (let i = 0; i < words.length; i++) {\n const word = words[i];\n const hash = this.simpleHash(word);\n const index = Math.abs(hash) % embedding.length;\n embedding[index] += 1 / Math.sqrt(words.length);\n }\n \n // Normalize the embedding\n const magnitude = Math.sqrt(embedding.reduce((sum, val) => sum + val * val, 0));\n return embedding.map(val => magnitude > 0 ? val / magnitude : 0);\n }\n\n private simpleHash(str: string): number {\n let hash = 0;\n for (let i = 0; i < str.length; i++) {\n const char = str.charCodeAt(i);\n hash = ((hash << 5) - hash) + char;\n hash = hash & hash; // Convert to 32-bit integer\n }\n return hash;\n }\n\n private 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 const magnitude = Math.sqrt(normA) * Math.sqrt(normB);\n return magnitude > 0 ? dotProduct / magnitude : 0;\n }\n\n private systemToText(system: SOARESystem): string {\n return `System: ${system.name}\nDescription: ${system.description}\nStatus: ${system.status}\nGoals: ${system.goals.map(g => g.description).join(', ')}\nComponents: ${system.components.map(c => `${c.name} (${c.type})`).join(', ')}\nCreated: ${system.createdAt.toISOString()}`;\n }\n\n private componentToText(component: SystemComponent): string {\n return `Component: ${component.name}\nType: ${component.type}\nStatus: ${component.status}\nDependencies: ${component.dependencies.join(', ')}\nConfig: ${JSON.stringify(component.config)}\nGenerated by: ${component.generatedBy}`;\n }\n\n private analyzeTrends(events: VectorEntry[]): string[] {\n if (events.length < 3) return [];\n \n // Group events by hour to identify trends\n const hourlyEvents = events.reduce((acc, event) => {\n const hour = new Date(event.metadata.timestamp).getHours();\n acc[hour] = (acc[hour] || 0) + 1;\n return acc;\n }, {} as Record);\n \n const trends: string[] = [];\n const hours = Object.keys(hourlyEvents).map(Number).sort();\n \n // Simple trend detection\n if (hours.length >= 3) {\n const recent = hours.slice(-3);\n const counts = recent.map(h => hourlyEvents[h]);\n \n if (counts[2] > counts[1] && counts[1] > counts[0]) {\n trends.push(\"Event frequency is increasing\");\n } else if (counts[2] < counts[1] && counts[1] < counts[0]) {\n trends.push(\"Event frequency is decreasing\");\n }\n }\n \n return trends;\n }\n\n private async persistVectors(): Promise {\n try {\n const vectors = Array.from(this.cache.values());\n await blob.setJSON(\"soare_vectors\", vectors);\n } catch (error) {\n console.error(\"Failed to persist vectors:\", error);\n }\n }\n\n async cleanup(olderThanDays = 30): Promise {\n await this.initialize();\n \n const cutoff = new Date(Date.now() - olderThanDays * 24 * 60 * 60 * 1000);\n let removed = 0;\n \n for (const [id, entry] of this.cache.entries()) {\n if (entry.metadata.timestamp < cutoff && entry.metadata.type === 'event') {\n this.cache.delete(id);\n removed++;\n }\n }\n \n if (removed > 0) {\n await this.persistVectors();\n }\n \n return removed;\n }\n}","deleted","label",[167,168,169,149],"backend","core","memory","permalink","https://www.val.town/x/sexy606ai/SOARE/code/backend/core/memory/vector-store.ts","extension","actionData","errors"]