More memory optimizations
This commit is contained in:
+1
-1
@@ -1,6 +1,6 @@
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{
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"name": "@ztimson/ai-utils",
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"version": "1.7.1",
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"version": "1.7.2",
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"description": "AI Utility library",
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"author": "Zak Timson",
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"license": "MIT",
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+5
-2
@@ -1,11 +1,14 @@
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export * from './ai';
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export * from './antrhopic';
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export * from './audio';
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export * from './helpers';
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export * from './llm';
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export * from './memory';
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export * from './memory/graph';
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export * from './memory/kd-tree';
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export * from './memory/memory';
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export * from './memory/memory-state';
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export * from './open-ai';
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export * from './provider';
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export * from './token-pool'
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export * from './tools';
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export * from './vision';
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export * from './utils';
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+4
-20
@@ -1,17 +1,19 @@
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import {clean, makeUnique, snakeCase} from '@ztimson/utils';
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import {AbortablePromise, Ai} from './ai.ts';
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import {Anthropic} from './antrhopic.ts';
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import {MemoryCache} from './memory/memory-state.ts';
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import {Memory, MemoryManager, MemoryOptions} from './memory/memory.ts';
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import {OpenAi} from './open-ai.ts';
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import {LLMProvider} from './provider.ts';
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import {AiTool, AiToolArg} from './tools.ts';
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import {fileURLToPath} from 'url';
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import {spawn} from 'node:child_process';
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import {Memory, MemoryCache, MemoryManager, MemoryOptions, stripHeader} from './memory.ts';
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import {mkdtempSync} from 'node:fs';
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import fs from 'node:fs/promises';
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import {tmpdir} from 'node:os';
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import {dirname, join, basename, extname} from 'path';
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import { PDFParse } from 'pdf-parse';
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import {stripHeader} from './utils.ts';
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const MAX_AGENT_DEPTH = 5;
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const PDF_OCR_PAGE_THRESHOLD = 12; // above this many pages, OCR scanned pages instead of feeding images to the model
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@@ -501,7 +503,7 @@ Description: ${r.description}
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Linked: ${makeUnique([...r.links, ...r.backlinks]).join(', ')}
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<!-- Truncated -->`).join('\n\n') : ''}`.trim())
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}
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if(mem.tool) tools.push(this.memoryManager.tools.read(mem.memory));
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if(mem.tool) tools.push(...this.memoryManager.tools.read(mem.memory));
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}
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}
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@@ -594,24 +596,6 @@ Linked: ${makeUnique([...r.links, ...r.backlinks]).join(', ')}
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return h;
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}
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/**
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* Compare the difference between embeddings (calculates the angle between two vectors)
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* @param {number[]} v1 First embedding / vector comparison
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* @param {number[]} v2 Second embedding / vector for comparison
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* @returns {number} Similarity values 0-1: 0 = unique, 1 = identical
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*/
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cosineSimilarity(v1: number[], v2: number[]): number {
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if (v1.length !== v2.length) throw new Error('Vectors must be same length');
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let dotProduct = 0, normA = 0, normB = 0;
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for (let i = 0; i < v1.length; i++) {
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dotProduct += v1[i] * v2[i];
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normA += v1[i] * v1[i];
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normB += v2[i] * v2[i];
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}
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const denominator = Math.sqrt(normA) * Math.sqrt(normB);
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return denominator === 0 ? 0 : dotProduct / denominator;
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}
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/**
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* Chunk text into parts for AI digestion
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* @param {object | string} target Item that will be chunked (objects get converted)
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@@ -1,111 +0,0 @@
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import {memoryStore} from '../memory.ts';
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import {Memory, MemoryStore} from './memory.ts';
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import {LLMRequest} from '../llm.ts';
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import {embedMemoryFields, journalDescription, stripHeader, touchHeader} from '../utils.ts';
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export const PENDING_HEADING = '## Pending';
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export const TODO_HEADING = '## Todo List';
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export async function docAgent(llm: any, node: Memory, memories: MemoryStore, options: LLMRequest, entry?: {request: {abort?: () => void} | null},): Promise<void> {
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const store = memoryStore(memories);
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if(!store.list.includes(node)) return;
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const currentBody = stripHeader(node.content);
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const journal = node.name.startsWith('Journal/');
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const system = `You maintain a Markdown ${journal ? 'journal' : 'wiki file'}, produce the final document body
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- Incorporate the \`${PENDING_HEADING}\` into the wiki & remove it. These new facts trump conflicting information
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- Preserve existing structure, wording and context unless directly affected by \`${PENDING_HEADING}\` information
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- Only restructure if clearly unorganized, or there is duplicate, stale or misleading information. Do not restructure merely because you dont like it
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- Let the structure fit the document; do not force a template
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- Use headings, subheadings, lists, tables, code blocks and other formatting where useful
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- Create [[WikiLinks]] to existing pages or ghost nodes when the relationship is meaningful but does not exist yet
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- Remove duplication and obsolete information, preserve useful detail
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- Do not invent facts or make unnecessary changes
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- Never add edit commentary` + (journal ? `
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- This is a JOURNAL document, focus on creating a chronological report of events
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- Keep events chronological and concise. Do not reorganize events into
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- subject-based wiki sections. Preserve dates and useful temporal context.
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Rough Template:
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\`\`\`markdown
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# Journal
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## ${TODO_HEADING}
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- [ ] ...
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### YYYY-MM-DD
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- ...
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\`\`\`` : `
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- Use only sections that have content and make sense
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- Keep related facts together
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- historical context may be preserved
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- Use ## Related for meaningful links to other wiki pages.
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Rough Template:
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# Title
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## ${TODO_HEADING}
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- [ ] ...
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## Overview
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...
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## Heading
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...
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### Subheading
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...
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## Related
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- [[wikiLinks]]: relationship description
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`) + `
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Available pages for wikilinks:
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${store.list.filter(n => n.name !== node.name).map(n => n.name).join(', ') || 'none'}`;
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let update;
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try {
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for(let i = 0; i < 2 && !update?.content; i++) {
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const request = llm.ask(currentBody, {
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model: options.model,
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temperature: 0.3,
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schema: {
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description: {
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type: 'string',
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description: 'One factual sentence describing the document\'s ENTIRE SUBJECT MATTER — for use as a search/merge fingerprint',
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required: true,
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},
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content: {
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type: 'string',
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description: 'Rewritten document body in markdown, without the frontmatter block',
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required: true,
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},
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},
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system,
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});
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if(entry) entry.request = request;
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update = await request;
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}
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} catch(err: any) {
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if(err?.name === 'AbortError') return;
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throw err;
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} finally {
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if(entry) entry.request = null;
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}
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if(!update?.content) return;
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node.description = node.name.startsWith('Journal/')
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? journalDescription(node.name)
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: node.name !== 'People/User'
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? update.description.replaceAll(/[\n:]/g, '')
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: 'All information about the current user';
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node.content = touchHeader(node, update.content);
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await embedMemoryFields(node, llm);
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}
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@@ -1,154 +0,0 @@
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import {MemoryCache, memoryStore} from '../memory.ts';
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import {LLMRequest} from '../llm.ts';
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import {Memory, MemoryStore} from './memory.ts';
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const ALIAS_MATCH_THRESHOLD = 0.55;
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export type FactBucket = {
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subject: string;
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facts: string[];
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}
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export type MemoryTask = {
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subject: string;
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task: string;
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done: boolean;
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}
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export type FactAgentResult = {
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buckets: FactBucket[];
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journal: string;
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tasks: MemoryTask[];
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}
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function dedupeFacts(facts: string[]): string[] {
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const seen = new Map<string, string>();
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for(const f of facts) {
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const clean = f.trim();
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if(clean) seen.set(clean.toLowerCase(), clean);
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}
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return [...seen.values()];
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}
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export function resolveSubject(subject: string, memories: Memory[] | MemoryCache, llm: any,): string {
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function normalize(name: string): string {
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return name.trim().toLowerCase().replace(/\s+/g, ' ');
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}
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const list = memories instanceof MemoryCache ? memories.memories : memories;
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const trimmed = subject.trim();
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const exact = list.find(m => m.name === trimmed);
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if(exact) return exact.name;
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const normalized = normalize(trimmed);
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const caseInsensitive = list.find(m => normalize(m.name) === normalized);
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if(caseInsensitive) return caseInsensitive.name;
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const root = trimmed.split('/')[0];
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const leaf = trimmed.split('/').slice(1).join('/') || trimmed;
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const candidates = list.filter(m => m.name.split('/')[0] === root && m.name !== trimmed);
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if(!candidates.length) return trimmed;
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const leaves = candidates.map(m => m.name.split('/').slice(1).join('/') || m.name);
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const probe = leaves.length > 1 ? leaves : [...leaves, ''];
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const {max, similarities} = llm.fuzzyMatch(leaf, ...probe);
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if(max >= ALIAS_MATCH_THRESHOLD) return candidates[similarities.indexOf(max)].name;
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return trimmed;
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}
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export async function factAgent(llm: any, conversation: string, memories: MemoryStore, options: LLMRequest,): Promise<FactAgentResult> {
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const store = memoryStore(memories);
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const ghosts = store.ghosts();
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const response = await llm.ask(conversation, {
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model: options.model,
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temperature: 0.2,
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system: `Turn this conversation into a persistent memory file by extracting information into organized bullet points
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Think of this like an Obsidian vault with a clear division of responsibility:
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- The JOURNAL is a chronological log. It answers "what happened, and when" and is the only place with a sense of time.
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- ENTITY DOSSIERS are a wiki pages. They answer "what is currently true about this subject", with no sense of time — only current state.
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- Never blur the two: a one-off event, conversation, or debugging session is a journal entry, not an entity, even if it's detailed.
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- Never extract this assistant's own tools, capabilities, or system behavior — that's not memory, it's spec.
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- Skip greetings, pleasantries, small talk and generic exchanges entirely.
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1. Journal Log
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- A chronological, skimmable log of what actually happened: high level discussions, decisions made (including ones reached jointly with the assistant), progress on projects, problems worked through
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- This is NOT a transcript, and it is NOT a step-by-step record, its a compressed day-to-day log of notable events & developments
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- One line per development is usually enough: what was worked on and the outcome, not the blow-by-blow of how
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- Skip anything that's a todo item (goes in Todo Tasks) or a durable fact about a subject (goes in Entity Dossiers)
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2. Todo Tasks
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- Extract concrete tasks the user says need to be done, should be done, or were completed
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- Return the task text and whether it is still todo or is done
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- A completed task should be marked done, not recreated as a new todo
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- Only extract actionable tasks, not general goals or observations, if none - omit returning a tasks array
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- Assign each task a HOME ENTITY subject using the same rules as Entity Dossiers below, or an empty string if it belongs in the journal: personal/life task (reach out to someone, pay a bill, etc.)
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3. Entity Dossiers
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- Detailed dossiers with all factual information regarding a subject
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- Only extract facts the USER explicitly stated about themselves, their work, projects, or decisions made during this conversation — not assistant claims, guesses, or temporary/debugging details
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- Record the final/end state, not intermediate deltas
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- NEVER create a dossier for something I wouldn't find on a wiki: temporary info, debugging, guesses, conversation fragments (that's journal material)
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Entity Dossier Routing — every fact belongs to exactly one HOME ENTITY: the [pro]noun that OWNS the fact, not the initializer
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- All facts primarily about the user go under People/User
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- Path format is always Collection/Subject (People/Sarah, Projects/Oxide) — never a bare/rootless name
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- ALWAYS prefer an existing node (including ghosts) over creating a new one, even under a different alias — route the facts there, and record the unused name as a ghost node
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- Only use child paths (Collection/Subject/Aspect) when a clear child/parent relationship exists between entities
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- Use [[WikiLinks]] to express relationships between entities. Ghost links are supported so NEVER create a document just to hold a relationship
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Example Entity Naming Convention:
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- Projects/[Name]
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- People/[Name]
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- History/[Name]
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- Science/[Name]
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- [Subject]/[Name]
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- Class/[Name]/[Chapter]
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Available nodes:
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${store.list.map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}
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${ghosts.length ? `${ghosts.map(g => `- ${g}: (Ghost)`).join('\n')}` : ''}`,
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schema: {
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journal: {
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type: 'string',
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description: 'Short bullet point recap, omit if nothing notable happened',
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},
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tasks: {
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type: 'array',
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description: 'Concrete tasks mentioned or completed in the conversation, omit if none',
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items: {type: 'object', items: {
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subject: {type: 'string', description: 'Exact node name / new persistent entity path this task belongs to, or an empty string if this is a personal task with no entity of its own (those go in the journal)', required: true,},
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task: {type: 'string', description: 'Concise actionable task', required: true,},
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done: {type: 'boolean', description: 'Whether the task is completed', required: true,},
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}},
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},
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buckets: {
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type: 'array',
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description: 'Groups of facts to remember; omit if none',
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items: {type: 'object', items: {
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subject: {type: 'string', description: 'Exact node name or new persistent entity path', required: true,},
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facts: {type: 'array', description: 'Facts to store here', items: {type: 'string'},},
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}},
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},
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},
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});
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const buckets = new Map<string, string[]>();
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for(const bucket of response.buckets ?? []) {
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const subject = bucket.subject.trim();
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const facts = buckets.get(subject) ?? [];
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facts.push(...dedupeFacts(bucket.facts));
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buckets.set(subject, facts);
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}
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return {
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buckets: buckets.entries().toArray().map(([subject, facts]) => ({subject, facts})),
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journal: (response.journal ?? '').trim(),
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tasks: response.tasks ?? [],
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};
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}
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@@ -1,4 +1,5 @@
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import {Memory, MemoryCache} from './memory.ts';
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import {MemoryCache} from './memory-state.ts';
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import type {Memory} from './memory.ts';
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export type MemoryNode = {
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name: string;
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@@ -13,14 +14,6 @@ export function extractLinks(content: string): string[] {
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return [...new Set([...matches].map(m => m[1].trim()))];
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}
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/**
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* Incrementally patch the graph for a set of changed memories, instead of
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* re-scanning every document. Only the changed memories' own content is
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* re-parsed for links; affected targets have their backlinks patched.
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* Does NOT handle node deletion — full rebuildGraph() is still required
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* when a memory is removed, since that needs a backlink sweep across
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* everyone who might reference it.
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*/
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export function patchGraph(mems: Memory[], nodes: MemoryNode[], changed: Memory[]): MemoryNode[] {
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const nameSet = new Set(mems.map(m => m.name));
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const byName = new Map(nodes.map(n => [n.name, n]));
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@@ -1,79 +0,0 @@
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import {memoryStore} from '../memory.ts';
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import {Memory, MemoryStore} from './memory.ts';
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import {LLMRequest} from '../llm.ts';
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export type LibrarianAction =
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| {type: 'merge'; source: string; target: string; instructions?: string}
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| {type: 'split'; target: string; documents: {name: string; instructions?: string}[]}
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| {type: 'move'; source: string; target: string}
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| {type: 'delete'; target: string}
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| {type: 'link'; source: string; target: string};
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export type LibrarianResult = {
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actions: LibrarianAction[];
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};
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export async function librarianAgent(llm: any, node: Memory, memories: MemoryStore, options: LLMRequest, limit = 8,): Promise<LibrarianResult> {
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if(node.name.startsWith('Journal/')) return {actions: []};
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const store = memoryStore(memories);
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const candidates = store.search(node.embedding, limit + 1)
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.filter(result => result.name !== node.name && !result.name.startsWith('Journal/'))
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.map(result => store.find(result.name))
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.filter((memory): memory is Memory => !!memory);
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if(!candidates.length) return {actions: []};
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const documents = [node, ...candidates];
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const response = await llm.ask('', {
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model: options.model,
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temperature: 0.2,
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schema: {
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actions: {
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type: 'array',
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description: 'Actions needed to improve the organization of the knowledge base. Omit if no changes are needed.',
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items: {type: 'object', items: {
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type: {type: 'string', description: 'One of: merge, split, move, delete, link', required: true,},
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source: {type: 'string', description: 'Source document name. Required for merge, move, and link.',},
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target: {type: 'string', description: 'Target document name. Required for merge, move, delete, and link.',},
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instructions: {type: 'string', description: 'Specific instructions for performing the action.',},
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documents: {type: 'array', description: 'Documents to create when splitting a document.', items: {type: 'object', items: {
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name: {type: 'string', required: true},
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instructions: {type: 'string'},
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}}}},
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},
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},
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},
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system: `You are a librarian of a wiki. You keep it organized, coherent, and easy to navigate. You DONT rewrite pages but rather, recommend actions to organize the wiki
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Available actions:
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- merge: Two pages represent the same entity and should be merged together
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- split: One page contains significant information on two distinct entities and should become separate pages
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- move: A document belongs under a different name or path for consistancy
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- delete: A document is obsolete, accidental or contains nothing redeamable
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- link: Two distinct entities are meaningfully related and should reference each other
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Rules:
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- Only act on high confidence
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||||
- Semantic similarity alone is NOT sufficient reason to merge two distinct entities
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||||
- Prefer preserving existing entities and paths when possible
|
||||
- ALWAYS use & preserve [[wikilinks]] (including ghost nodes and journal entries) to represent relationships
|
||||
- A document should represent one persistent entity, not a temporal state, feature, bug, event, decision, or conversation
|
||||
- When merging, use the more common name as the destination and use ghost nodes to reference old aliases
|
||||
- When splitting, each resulting document must represent a distinct persistent entity
|
||||
- Return an empty actions array when the documents are already organized correctly
|
||||
|
||||
Documents being reviewed:
|
||||
|
||||
${documents.map(memory => `### ${memory.name}
|
||||
Description: ${memory.description}
|
||||
|
||||
Links: ${[...memory.links, ...memory.backlinks].join(', ') || 'none'}
|
||||
|
||||
\`\`\`markdown
|
||||
${memory.content}
|
||||
\`\`\``).join('\n\n')}`,
|
||||
});
|
||||
|
||||
return {
|
||||
actions: response.actions ?? [],
|
||||
};
|
||||
}
|
||||
@@ -1,7 +1,7 @@
|
||||
import {MemoryNode, patchGraph, rebuildGraph} from './helpers.ts';
|
||||
import {KDTree} from './memory/kd-tree.ts';
|
||||
import {Memory, MemoryRef, MemoryStore} from './memory/memory.ts';
|
||||
import {cosineDistance, embedMemoryFields} from './utils.ts';
|
||||
import {MemoryNode, patchGraph, rebuildGraph} from './graph.ts';
|
||||
import {KDTree} from './kd-tree.ts';
|
||||
import type {Memory, MemoryRef, MemoryStore} from './memory.ts';
|
||||
import {cosineDistance, embedMemoryFields} from '../utils.ts';
|
||||
|
||||
const TREE_TOMBSTONE_LIMIT = 0.25;
|
||||
|
||||
@@ -45,6 +45,7 @@ export function memoryStore(memories: MemoryStore): {
|
||||
forget: name => {
|
||||
const idx = memories.findIndex(m => m.name === name);
|
||||
if(idx === -1) return false;
|
||||
|
||||
memories.splice(idx, 1);
|
||||
return true;
|
||||
},
|
||||
@@ -54,14 +55,7 @@ export function memoryStore(memories: MemoryStore): {
|
||||
if(!missing.length) return 0;
|
||||
|
||||
await Promise.all(missing.map(async node => {
|
||||
const body = node.content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
|
||||
const [titleE] = await llm.embedding(node.name.split('/').pop() || node.name);
|
||||
const [descE] = await llm.embedding(node.description || '');
|
||||
const bodyChunks = body ? await llm.embedding(body) : [];
|
||||
|
||||
if(titleE) node.titleEmbedding = titleE.embedding;
|
||||
if(descE) node.embedding = descE.embedding;
|
||||
node.bodyEmbeddings = bodyChunks.map((c: any) => c.embedding).filter(Boolean);
|
||||
await embedMemoryFields(node, llm);
|
||||
}));
|
||||
|
||||
return missing.length;
|
||||
@@ -94,6 +88,7 @@ export class MemoryCache {
|
||||
|
||||
for(const [name, emb] of [...this.indexed]) {
|
||||
const mem = this.memories.find(m => m.name === name);
|
||||
|
||||
if(!mem || !current.has(name) || mem.embedding !== emb) {
|
||||
this.tree.remove(p => p.name === name);
|
||||
this.indexed.delete(name);
|
||||
@@ -102,21 +97,36 @@ export class MemoryCache {
|
||||
|
||||
for(const mem of this.memories) {
|
||||
if(!mem.embedding?.length || this.indexed.has(mem.name)) continue;
|
||||
if(this.tree.dims === 0) this.tree = new KDTree<MemoryRef>(mem.embedding.length, 'cosine');
|
||||
|
||||
if(this.tree.dims === 0) {
|
||||
this.tree = new KDTree<MemoryRef>(mem.embedding.length, 'cosine');
|
||||
}
|
||||
|
||||
if(mem.embedding.length !== this.tree.dims) continue;
|
||||
|
||||
this.tree.insert({
|
||||
vector: mem.embedding,
|
||||
payload: {name: mem.name, description: mem.description},
|
||||
payload: {
|
||||
name: mem.name,
|
||||
description: mem.description,
|
||||
},
|
||||
});
|
||||
|
||||
this.indexed.set(mem.name, mem.embedding);
|
||||
}
|
||||
|
||||
if(this.tree.tombstoneRatio > TREE_TOMBSTONE_LIMIT) this.tree.rebalance();
|
||||
if(this.tree.tombstoneRatio > TREE_TOMBSTONE_LIMIT) {
|
||||
this.tree.rebalance();
|
||||
}
|
||||
}
|
||||
|
||||
search(query: number[], limit: number): MemoryRef[] {
|
||||
if(!this.tree || this.tree.dims === 0) return [];
|
||||
return this.tree.knn(query, limit).map(r => ({...r.point.payload, distance: r.distance}));
|
||||
|
||||
return this.tree.knn(query, limit).map(r => ({
|
||||
...r.point.payload,
|
||||
distance: r.distance,
|
||||
}));
|
||||
}
|
||||
|
||||
add(memory: Memory): void {
|
||||
@@ -126,14 +136,17 @@ export class MemoryCache {
|
||||
|
||||
update(memory: Memory): void {
|
||||
const existing = this.find(memory.name);
|
||||
|
||||
if(existing) Object.assign(existing, memory);
|
||||
else this.memories.push(memory);
|
||||
|
||||
this.rebuild([existing ?? memory]);
|
||||
}
|
||||
|
||||
remove(name: string): boolean {
|
||||
const idx = this.memories.findIndex(m => m.name === name);
|
||||
if(idx === -1) return false;
|
||||
|
||||
this.memories.splice(idx, 1);
|
||||
this.rebuild();
|
||||
return true;
|
||||
@@ -147,6 +160,7 @@ export class MemoryCache {
|
||||
this.nodes = changed?.length && this.nodes.length
|
||||
? patchGraph(this.memories, this.nodes, changed)
|
||||
: rebuildGraph(this.memories);
|
||||
|
||||
this.syncTree();
|
||||
return this.nodes;
|
||||
}
|
||||
@@ -158,8 +172,10 @@ export class MemoryCache {
|
||||
async backfillEmbeddings(llm: any): Promise<number> {
|
||||
const missing = this.memories.filter(m => !m.embedding?.length);
|
||||
if(!missing.length) return 0;
|
||||
|
||||
await Promise.all(missing.map(node => embedMemoryFields(node, llm)));
|
||||
this.commit(missing);
|
||||
|
||||
return missing.length;
|
||||
}
|
||||
}
|
||||
+321
-1
@@ -1,4 +1,12 @@
|
||||
import {MemoryCache} from '../memory.ts';
|
||||
import {AiTool} from '../tools.ts';
|
||||
import type {LLMMessage, LLMRequest} from '../llm.ts';
|
||||
import {MemoryCache, memoryStore} from './memory-state.ts';
|
||||
import {cosineDistance, embedMemoryFields, stripHeader, updateMemory} from '../utils.ts';
|
||||
|
||||
const FACT_SIMILARITY_THRESHOLD = 0.62;
|
||||
const DUPLICATE_THRESHOLD = 0.68;
|
||||
const PROTECTED_MEMORIES = ['People/User'];
|
||||
const COLLECTION_WORDS = ['project', 'projects', 'people', 'person', 'managed', 'guides', 'guide', 'research', 'class', 'classes'];
|
||||
|
||||
export type Memory = {
|
||||
name: string;
|
||||
@@ -26,3 +34,315 @@ export type MemoryOptions = {
|
||||
}
|
||||
|
||||
export type MemoryStore = Memory[] | MemoryCache;
|
||||
|
||||
/** Create an empty memory shell. */
|
||||
function emptyNode(name: string, description = ''): Memory {
|
||||
return {name, description, content: `# ${name.split('/').pop()}\n`, embedding: [], links: [], backlinks: []};
|
||||
}
|
||||
|
||||
function renderNode(node: Memory): string {
|
||||
return `### ${node.name}
|
||||
Description: ${node.description}
|
||||
Links: ${[...node.links, ...node.backlinks].join(', ') || 'none'}
|
||||
|
||||
\`\`\`markdown
|
||||
${node.content}
|
||||
\`\`\``;
|
||||
}
|
||||
|
||||
function factSimilarity(a: Memory, b: Memory): number {
|
||||
return !a.bodyEmbeddings?.length || !b.bodyEmbeddings?.length ? 0 : Math.max(...a.bodyEmbeddings.flatMap(av => b.bodyEmbeddings!.map(bv => 1 - cosineDistance(av, bv))));
|
||||
}
|
||||
|
||||
function words(text: string): string[] {
|
||||
return [...new Set(text.toLowerCase().replace(/[[\]()/_-]/g, ' ').replace(/[^a-z0-9\s]/g, '').split(/\s+/).filter(w => w && !COLLECTION_WORDS.includes(w)))];
|
||||
}
|
||||
|
||||
function jaccard(a: string[], b: string[]): number {
|
||||
const bs = new Set(b), hit = a.filter(x => bs.has(x)).length, total = new Set([...a, ...b]).size;
|
||||
return total ? hit / total : 0;
|
||||
}
|
||||
|
||||
function duplicateScore(a: Memory, b: Memory): number {
|
||||
const name = Math.max(
|
||||
jaccard(words(a.name), words(b.name)),
|
||||
jaccard(words(a.name.split('/').pop() || a.name), words(b.name.split('/').pop() || b.name)),
|
||||
);
|
||||
const desc = jaccard(words(a.description), words(b.description));
|
||||
const body = factSimilarity(a, b);
|
||||
const emb = a.embedding?.length && b.embedding?.length && a.embedding.length === b.embedding.length ? 1 - cosineDistance(a.embedding, b.embedding) : 0;
|
||||
return Math.max(body, name * 0.9 + desc * 0.06 + emb * 0.04, emb * 0.55 + name * 0.35 + desc * 0.1);
|
||||
}
|
||||
|
||||
function homeScore(node: Memory): number {
|
||||
return (PROTECTED_MEMORIES.includes(node.name) ? 1e9 : 0)
|
||||
+ (node.name.includes('/') ? 4 : 0)
|
||||
+ (node.description && node.description !== 'Persistent memory document' ? 1 : 0)
|
||||
+ Math.min(stripHeader(node.content).length / 1000, 5);
|
||||
}
|
||||
|
||||
function pickMerge(a: Memory, b: Memory, touched: Set<string>): [drop: Memory, home: Memory] {
|
||||
const as = homeScore(a), bs = homeScore(b);
|
||||
if(touched.has(a.name) && !touched.has(b.name)) return as > bs + 2 ? [b, a] : [a, b];
|
||||
if(touched.has(b.name) && !touched.has(a.name)) return bs > as + 2 ? [a, b] : [b, a];
|
||||
return as <= bs ? [a, b] : [b, a];
|
||||
}
|
||||
|
||||
/** Build memory tools and memory index text. */
|
||||
export function memoryTools(llm: any, memories: MemoryStore): {tools: AiTool[]; list: string} {
|
||||
const store = memoryStore(memories);
|
||||
const names = new Map<string, string>();
|
||||
|
||||
for(const node of store.list)
|
||||
if(!names.has(node.name)) names.set(node.name, `${node.name} - ${node.description}`);
|
||||
|
||||
for(const name of store.ghosts())
|
||||
if(!names.has(name)) names.set(name, `${name} - ghost node`);
|
||||
|
||||
return {
|
||||
list: [...names.values()].join('\n'),
|
||||
tools: [
|
||||
{
|
||||
name: 'memory_search',
|
||||
description: 'Semantically search memories for most relevant',
|
||||
args: {
|
||||
query: {type: 'string', description: 'Search query', required: true},
|
||||
limit: {type: 'number', description: 'Maximum results, default 5', default: 5},
|
||||
},
|
||||
fn: async ({query, limit = 5}) => {
|
||||
if(!query?.trim()) return 'Search query is required.';
|
||||
const [chunk] = await llm.embedding(query, {maxTokens: 8000, overlapTokens: 0});
|
||||
if(!chunk?.embedding) return 'Failed to create embedding from query';
|
||||
const results = store.search(chunk.embedding, limit).map(ref => store.find(ref.name)).filter((node): node is Memory => !!node);
|
||||
return results.length ? results.map(renderNode).join('\n\n---\n\n') : 'No relevant memories found.';
|
||||
},
|
||||
},
|
||||
{
|
||||
name: 'memory_read',
|
||||
description: 'Read an entire memory document by name',
|
||||
args: {name: {type: 'string', description: 'Exact document name', required: true}},
|
||||
fn: async ({name}) => {
|
||||
const node = store.find(name);
|
||||
return node ? renderNode(node) : store.ghosts().includes(name) ? `"${name}" is a ghost node with no document of its own.` : `Not found: "${name}".`;
|
||||
},
|
||||
},
|
||||
{
|
||||
name: 'memory_delete',
|
||||
description: 'Delete a duplicate or merged memory',
|
||||
args: {name: {type: 'string', description: 'Exact document name', required: true}},
|
||||
fn: async ({name}) => {
|
||||
store.forget(name);
|
||||
return `Removed: ${name}`;
|
||||
},
|
||||
},
|
||||
{
|
||||
name: 'memory_write',
|
||||
description: 'Create or replace a memory document.',
|
||||
args: {
|
||||
name: {type: 'string', description: 'Document name following the entity naming convention.', required: true},
|
||||
description: {type: 'string', description: 'One factual sentence describing the entire document subject', required: true},
|
||||
content: {type: 'string', description: 'Complete Markdown document body, including the # title', required: true},
|
||||
},
|
||||
fn: async (args: any) => {
|
||||
const name = String(args.name || '').trim();
|
||||
if(!name) return 'A document name is required.';
|
||||
const description = String(args.description || '').trim();
|
||||
if(!description) return 'A document description is required.';
|
||||
const content = String(args.content || '').trim();
|
||||
if(!content) return 'Document content is required.';
|
||||
|
||||
let node = store.find(name);
|
||||
if(!node) {
|
||||
node = emptyNode(name, description);
|
||||
if(store.cache) store.cache.add(node);
|
||||
else store.list.push(node);
|
||||
}
|
||||
|
||||
node.description = name === 'People/User' ? 'All information about the current user' : description.replace(/\s+/g, ' ').trim();
|
||||
node.content = updateMemory(node, content);
|
||||
await embedMemoryFields(node, llm);
|
||||
store.cache?.commit([node]);
|
||||
return `Updated ${name}`;
|
||||
},
|
||||
},
|
||||
],
|
||||
};
|
||||
}
|
||||
|
||||
export class MemoryManager {
|
||||
private memorized = new WeakMap<LLMMessage[], LLMMessage>();
|
||||
|
||||
constructor(private llm: any) {}
|
||||
|
||||
static normalize(memory?: Memory[] | MemoryCache | MemoryOptions): MemoryOptions | null {
|
||||
if(!memory) return null;
|
||||
if(Array.isArray(memory) || memory instanceof MemoryCache) return {memory, inject: true, tool: false, update: false};
|
||||
if(typeof memory === 'object' && 'memory' in memory) return {inject: true, tool: false, update: false, ...memory};
|
||||
return null;
|
||||
}
|
||||
|
||||
private memorySystem(list: string): string {
|
||||
return `You maintain notes written in markdown used for memories from recent conversations using your tools.
|
||||
Only preserve durable information worth remembering established by the USER.
|
||||
Do not store assistant guesses, speculation, suggestions, commentary, temporary state, or details that are not worth remembering.
|
||||
|
||||
## Rules
|
||||
- ALWAYS READ a target memory before changing it, \`memory_write\` does a full replace, it DOES NOT append!
|
||||
- Memories should contain the final state, not deltas
|
||||
- New conversational context is authoritative when it contracts existing information; reconcile it
|
||||
- Only remove information when stale, contradicted or duplicated; always preserve existing information, formatting and keep related information together
|
||||
- Only merge memories when two or more nodes are clearly about the same thing; only split a memory when it is clearly about two distinct subjects
|
||||
- Use [[WikiLinks]] liberally to record aliases and relationships between entities, even ones without pages yet (ghost nodes)
|
||||
- Use headings, subheadings, lists, tables and other markdown formatting to make documents clean
|
||||
- Maintain a \`## Todo List\` of checkboxes AS THE FIRST SUBHEADING when an entity has tasks
|
||||
- Only create todo items for USER tasks, not AI work
|
||||
- Only store each in one place, no duplicates
|
||||
- Use \`People/User\` for personal tasks or as a fallback
|
||||
|
||||
## Naming
|
||||
- Every fact should be grouped with the owning entity
|
||||
- Always follow the naming convention \`Collection/(Pro)Noun\`
|
||||
- Facts about the user belong under People/User
|
||||
- Reuse existing memories when they are clearly the same entity including aliases and ghost references.
|
||||
- Only create deeper paths when there is a real parent/child entity relationship: \`School/Class/Chapter\`
|
||||
|
||||
Valid Examples:
|
||||
- People/User
|
||||
- People/John Smith
|
||||
- Projects/Momentum
|
||||
- Projects/Momentum/Marketing
|
||||
- Research/Object Recognition
|
||||
- Guides/HAM Radio SOP
|
||||
|
||||
## Workflow
|
||||
|
||||
1. Create groups of durable information and todos based on the owning entity & naming rules above
|
||||
2. For each group:
|
||||
1. Read the existing memory(s)
|
||||
2. Merge the information & todos based on the rules above
|
||||
3. Write the entire patched document
|
||||
|
||||
Available memories:
|
||||
|
||||
${list || 'No memory documents exist yet.'}`;
|
||||
}
|
||||
|
||||
private touchedNames(history: LLMMessage[]): string[] {
|
||||
return [...new Set(history
|
||||
.filter((h: any) => h.role === 'tool' && h.name === 'memory_write' && !h.error)
|
||||
.map((h: any) => String(h.args?.name || h.content?.match(/^Updated (.+)$/)?.[1] || '').trim())
|
||||
.filter(Boolean))];
|
||||
}
|
||||
|
||||
private async backfillEmbeddings(store: ReturnType<typeof memoryStore>): Promise<void> {
|
||||
const missing = store.list.filter(m => !m.embedding?.length || !m.titleEmbedding?.length || !m.bodyEmbeddings?.length);
|
||||
await Promise.all(missing.map(m => embedMemoryFields(m, this.llm)));
|
||||
store.cache?.commit(missing);
|
||||
}
|
||||
|
||||
private closestDuplicate(node: Memory, store: ReturnType<typeof memoryStore>): Memory | null {
|
||||
return store.list
|
||||
.filter(m => m.name !== node.name && !m.name.startsWith('Journal/') && !node.name.startsWith('Journal/'))
|
||||
.map(m => ({node: m, score: duplicateScore(node, m)}))
|
||||
.filter(x => x.score >= DUPLICATE_THRESHOLD || factSimilarity(node, x.node) >= FACT_SIMILARITY_THRESHOLD)
|
||||
.sort((a, b) => b.score - a.score)[0]?.node || null;
|
||||
}
|
||||
|
||||
private async rehomeDeleted(drop: Memory, home: Memory, memories: MemoryStore, options: LLMRequest): Promise<void> {
|
||||
const store = memoryStore(memories);
|
||||
const backup = structuredClone(drop);
|
||||
store.forget(drop.name);
|
||||
|
||||
try {
|
||||
const memory = memoryTools(this.llm, memories);
|
||||
await this.llm.ask(`A duplicate memory document was removed automatically.
|
||||
|
||||
Deleted document:
|
||||
${renderNode(backup)}
|
||||
|
||||
Closest surviving home:
|
||||
${renderNode(home)}
|
||||
|
||||
Reinsert every durable unique fact, useful relationship, alias, and user todo from the deleted document into the best remaining memory document.
|
||||
Usually this should be "${home.name}", but use another existing memory if it is a better home.
|
||||
Read before writing. Write full replacement documents only.
|
||||
Do NOT recreate "${backup.name}" unless the deletion was wrong and it is clearly a distinct persistent entity.`, {
|
||||
model: options.memoryModel || options.model,
|
||||
temperature: 0.2,
|
||||
maxTokens: options.maxTokens,
|
||||
tools: memory.tools,
|
||||
history: [],
|
||||
system: this.memorySystem(memory.list),
|
||||
});
|
||||
} catch(err) {
|
||||
if(!store.find(backup.name)) store.cache ? store.cache.add(backup) : store.list.push(backup);
|
||||
throw err;
|
||||
} finally {
|
||||
store.cache?.commit(store.list);
|
||||
}
|
||||
}
|
||||
|
||||
private async reconcileSimilar(history: LLMMessage[], memories: MemoryStore, options: LLMRequest): Promise<void> {
|
||||
const store = memoryStore(memories);
|
||||
const touched = new Set(this.touchedNames(history));
|
||||
const targets = store.list.filter(m => touched.has(m.name) || [...touched].some(t => duplicateScore(m, store.find(t) || m) >= DUPLICATE_THRESHOLD));
|
||||
const deleted = new Set<string>();
|
||||
if(!targets.length) return;
|
||||
await this.backfillEmbeddings(store);
|
||||
|
||||
for(const node of targets) {
|
||||
if(!store.find(node.name) || deleted.has(node.name) || PROTECTED_MEMORIES.includes(node.name)) continue;
|
||||
const closest = this.closestDuplicate(node, store);
|
||||
if(!closest) continue;
|
||||
const [drop, home] = pickMerge(node, closest, touched);
|
||||
if(deleted.has(drop.name) || PROTECTED_MEMORIES.includes(drop.name)) continue;
|
||||
deleted.add(drop.name);
|
||||
await this.rehomeDeleted(drop, home, memories, options);
|
||||
await this.backfillEmbeddings(store);
|
||||
}
|
||||
}
|
||||
|
||||
async recollect(query: string, memory: MemoryStore, limit = 15): Promise<Memory[]> {
|
||||
const store = memoryStore(memory);
|
||||
if(!store.list.length || !query?.trim()) return [];
|
||||
const [chunk] = await this.llm.embedding(query, {maxTokens: 8000, overlapTokens: 0});
|
||||
return !chunk?.embedding ? [] : store.search(chunk.embedding, limit).map(ref => store.find(ref.name)).filter((m: Memory | undefined): m is Memory => !!m);
|
||||
}
|
||||
|
||||
get tools(): {read: (memory: MemoryStore) => AiTool[]} {
|
||||
return {read: (memory: MemoryStore) => memoryTools(this.llm, memory).tools};
|
||||
}
|
||||
|
||||
async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {},): Promise<Memory[]> {
|
||||
const store = memoryStore(memories);
|
||||
const previous = this.memorized.get(history);
|
||||
let start = 0;
|
||||
|
||||
if(previous) {
|
||||
const index = history.indexOf(previous);
|
||||
if(index >= 0) start = index + 1;
|
||||
}
|
||||
|
||||
const turns = history.slice(start).filter((h: any) => h.role === 'user' || h.role === 'assistant');
|
||||
const conversation = turns.map((h: any) => `[${h.role}]: ${h.content}`).join('\n\n').trim();
|
||||
if(!conversation) return store.list;
|
||||
|
||||
const memory = memoryTools(this.llm, memories);
|
||||
const memoryHistory: LLMMessage[] = [];
|
||||
|
||||
await this.llm.ask(conversation, {
|
||||
model: options.memoryModel || options.model,
|
||||
temperature: 0.2,
|
||||
maxTokens: options.maxTokens,
|
||||
tools: memory.tools,
|
||||
history: memoryHistory,
|
||||
system: this.memorySystem(memory.list),
|
||||
});
|
||||
|
||||
await this.reconcileSimilar(memoryHistory, memories, options);
|
||||
|
||||
const lastTurn = turns.at(-1);
|
||||
if(lastTurn) this.memorized.set(history, lastTurn);
|
||||
return store.list;
|
||||
}
|
||||
}
|
||||
|
||||
+2
-2
@@ -30,7 +30,7 @@ export function euclideanDistance(a: number[], b: number[]): number {
|
||||
return Math.sqrt(sum);
|
||||
}
|
||||
|
||||
function getWeekStart(date: Date = new Date()): string {
|
||||
export function getWeekStart(date: Date = new Date()): string {
|
||||
const d = new Date(Date.UTC(date.getFullYear(), date.getMonth(), date.getDate()));
|
||||
const day = d.getUTCDay();
|
||||
const diff = day === 0 ? -6 : 1 - day;
|
||||
@@ -72,7 +72,7 @@ export function stripHeader(content: string): string {
|
||||
return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
|
||||
}
|
||||
|
||||
export function touchHeader(node: Memory, body: string): string {
|
||||
export function updateMemory(node: Memory, body: string): string {
|
||||
const {fm} = parseFrontmatter(node.content);
|
||||
fm.set('name', node.name);
|
||||
fm.set('description', (node.name.startsWith('Journal/') ? journalDescription(node.name) : node.description)
|
||||
|
||||
Reference in New Issue
Block a user