Personal file memory organization instructions

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.2.4",
+	"version": "1.2.6",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",

src/llm.ts

@@ -229,8 +229,8 @@ ${r.content}
 	 * Digest full conversation history into memory documents.
 	 * Call on session end to persist the conversation.
 	 */
-	async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<void> {
+	async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<Memory[]> {
-		await this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
+		return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
 	}
 
 	/**

src/memory.ts

@@ -355,35 +355,32 @@ ${conversation}`,
 		return ordered.map(n => mem.find(m => m.name === n)!).filter(Boolean);
 	}
 
-	async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest): Promise<void> {
+	async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest): Promise<Memory[]> {
 		const conversation = history
 			.filter(h => h.role === 'user' || h.role === 'assistant')
 			.map(h => `[${h.role}]: ${h.content}`).join('\n\n').trim();
-
+		if(!conversation) return [];
-		if (!conversation) return;
-
-		const trackingId = `${Date.now()}_${Math.random()}`;
 
 		// Create and insert temp memory immediately
+		const trackingId = `${Date.now()}_${Math.random()}`;
 		const tempMemory = await this.createTempMemory(conversation);
 		const mem = memories instanceof MemoryCache ? memories.memories : memories;
 		mem.push(tempMemory);
-
+		if (memories instanceof MemoryCache) memories.rebuild();
-		if (memories instanceof MemoryCache) {
-			memories.rebuild();
-		}
-
 		this.pendingMemorizations.set(trackingId, {
 			memories,
 			tempMemoryName: tempMemory.name,
 			timestamp: Date.now(),
 		});
 
-		this._memorizeBackground(conversation, memories, options, trackingId)
+		try {
-			.catch(err => {
+			await this._memorizeBackground(conversation, memories, options);
-				console.error('[memorize] Background memorization failed:', err);
+			// Return the final memories (excluding temp ones)
-			})
+			const finalMem = memories instanceof MemoryCache ? memories.memories : memories;
-			.finally(() => {
+			return finalMem.filter(m => !m.name.startsWith('_temp_'));
+		} catch (err) {
+			throw err;
+		} finally {
 			// Remove temp memory from the exact same memory array/cache
 			const pending = this.pendingMemorizations.get(trackingId);
 			if (pending) {
@@ -393,30 +390,21 @@ ${conversation}`,
 				const idx = cleanMem.findIndex(m => m.name === pending.tempMemoryName);
 				if (idx !== -1) {
 					cleanMem.splice(idx, 1);
-						console.log(`[memorize] Removed temp memory: ${pending.tempMemoryName}`);
 				}
 				if (pending.memories instanceof MemoryCache) {
 					pending.memories.rebuild();
 				}
 			}
 			this.pendingMemorizations.delete(trackingId);
-			});
+		}
 	}
 
-	private async _memorizeBackground(conversation: string, memories: Memory[] | MemoryCache, options: LLMRequest, trackingId: string): Promise<void> {
+	private async _memorizeBackground(conversation: string, memories: Memory[] | MemoryCache, options: LLMRequest): Promise<void> {
 		const mem = memories instanceof MemoryCache ? memories.memories : memories;
 		const monday = getWeekMonday();
 		const sunday = getWeekSunday(monday);
-		const weekKey = monday;
+		const buckets = await this.factAgent(conversation, mem, options, monday);
-
+		if(!buckets.length) return;
-		console.log('[memorize] Starting fact extraction...');
-		const buckets = await this.factAgent(conversation, mem, options, weekKey);
-		console.log(`[memorize] Extracted ${buckets.length} buckets:`, buckets);
-
-		if (!buckets.length) {
-			console.log('[memorize] No facts extracted, exiting');
-			return;
-		}
 
 		const runDocAgent = (node: Memory, bucket: FactBucket, embedding?: number[], week?: {monday: string, sunday: string}) => {
 			if (memories instanceof MemoryCache) {
@@ -443,13 +431,10 @@ ${conversation}`,
 			await runDocAgent(node, bucket, embedding, week);
 		}));
 
-		if (memories instanceof MemoryCache) {
+		if(memories instanceof MemoryCache)
 			memories.rebuild();
 	}
 
-		console.log('[memorize] Completed successfully');
-	}
-
 	private buildHeader(node: Memory, week?: {monday: string, sunday: string}, links: string[] = [], backlinks: string[] = []): string {
 		const tags = node.name.split('/')[0]?.toLowerCase();
 		const lines = [
@@ -587,9 +572,6 @@ ${node.content || '(empty — this is a new document)'}
 
 	private async factAgent(conversation: string, memories: Memory[], options: LLMRequest, weekKey: string): Promise<FactBucket[]> {
 		const buckets: FactBucket[] = [];
-
-		console.log('[factAgent] Starting extraction...');
-
 		await this.llm.ask(conversation, {
 			model: options.model,
 			temperature: 0.2,
@@ -604,7 +586,8 @@ Rules:
 
 When extracting facts, you MUST also decide the exact destination path:
 - Use an existing node name if the facts clearly belong there
-- Create a new path following collection/subject format if needed (e.g., People/Sarah, Projects/Oxide)
+- All information primary about the user should go under "Personal/Subject" (e.g., Personal/Info, Personal/Todos)
+- When required, create a new path following collection/subject format (e.g., People/Sarah, Projects/Oxide)
 - For journal entries, use "journal" (will auto-route to Journal/${weekKey})
 
 Available nodes:
@@ -618,7 +601,6 @@ ${this.listNodes(memories).map(n => `- ${n.name}: ${n.description}`).join('\n')
 					create_new: {type: 'boolean', description: 'True if this is a new node that doesn\'t exist yet', required: true},
 				},
 				fn: (args: any) => {
-					console.log('[factAgent] Tool called with:', args);
 					const subject = args.destination.trim().toLowerCase() === 'journal'
 						? `Journal/${weekKey}`
 						: args.destination;
@@ -631,8 +613,6 @@ ${this.listNodes(memories).map(n => `- ${n.name}: ${n.description}`).join('\n')
 				},
 			}],
 		});
-
-		console.log(`[factAgent] Extracted ${buckets.length} buckets:`, buckets);
 		return buckets;
 	}
 }