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ai-utils/src/memory.ts
ztimson a7aec4ee29
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Improved memory prompt slightly
2026-07-30 16:00:03 -04:00

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import {LLMRequest, LLMMessage} from './llm.ts';
import {MemoryCache} from './memory-cache.ts';
import {AiTool} from './tools.ts';
export type Memory = {
name: string;
description: string;
content: string;
embedding: number[];
}
export type MemoryRef = {
name: string;
description: string;
}
export type FactBucket = {
subject: string;
facts: string[];
}
export type MemoryNode = {
name: string;
missing: boolean;
links: string[];
backlinks: string[];
}
function extractLinks(content: string): string[] {
if(!content) return [];
const matches = content.matchAll(/\[\[([^\]]+)\]\]/g);
return [...new Set([...matches].map(m => m[1].trim()))];
}
export function extractMetadata(content: string): {links: string[], backlinks: string[]} {
const match = content.match(/^---\n([\s\S]*?)\n---/);
if (!match) return {links: [], backlinks: []};
const fm = match[1];
const getList = (key: string): string[] => {
const m = fm.match(new RegExp(`^${key}:\\s*\\[(.*)\\]$`, 'm'));
if (!m || !m[1].trim()) return [];
return m[1].split(',').map(s => s.trim().replace(/^"|"$/g, '')).filter(Boolean);
};
return {
links: getList('links'),
backlinks: getList('backlinks'),
};
}
function dedupeFacts(facts: string[]): string[] {
const seen = new Map<string, string>();
for (const f of facts) {
const clean = f.trim();
if (clean) seen.set(clean.toLowerCase(), clean);
}
return [...seen.values()];
}
function cosineDistance(a: number[], b: number[]): number {
let dot = 0, normA = 0, normB = 0;
for (let i = 0; i < a.length; i++) {
dot += a[i] * b[i];
normA += a[i] * a[i];
normB += b[i] * b[i];
}
const denom = Math.sqrt(normA) * Math.sqrt(normB);
return denom === 0 ? 1 : 1 - dot / denom;
}
function getWeekMonday(date: Date = new Date()): string {
const d = new Date(Date.UTC(date.getFullYear(), date.getMonth(), date.getDate()));
const day = d.getUTCDay();
const diff = day === 0 ? -6 : 1 - day;
d.setUTCDate(d.getUTCDate() + diff);
return d.toISOString().slice(0, 10);
}
function getWeekSunday(monday: string): string {
const d = new Date(`${monday}T00:00:00Z`);
d.setUTCDate(d.getUTCDate() + 6);
return d.toISOString().slice(0, 10);
}
export class MemoryManager {
private pendingMemorizations = new Map<string, {
memories: Memory[] | MemoryCache,
tempMemoryName: string,
timestamp: number,
}>();
private queues = new Map<string, {
pending: string[],
request: {abort?: () => void} | null,
task: Promise<void>,
}>();
tools = {
read: (memories: Memory[] | MemoryCache): AiTool => ({
name: 'memory_recall',
description: 'Read the full content of a memory document',
args: {
name: {type: 'string', description: 'Exact memory name', required: true},
},
fn: (args: any) => {
const mems = memories instanceof MemoryCache ? memories.memories : memories;
const mem = mems.find(m => m.name === args.name);
if (!mem) return 'Document not found';
return mem.content;
},
}),
forget: (memories: Memory[] | MemoryCache): AiTool => ({
name: 'memory_forget',
description: 'Permanently delete a memory document and clean up all references to it',
args: {
name: {type: 'string', description: 'Exact memory name to forget', required: true}
},
fn: (args: any) => {
const result = this.forget(args.name, memories);
return result ? `Forgotten: ${args.name}` : `Not found: ${args.name}`;
},
}),
};
constructor(private llm: any) {}
private async createTempMemory(conversation: string): Promise<Memory> {
const timestamp = Date.now();
const content = `---
name: _temp_${timestamp}
description: Temporary memory - processing in background
tags: [_temporary]
links: []
backlinks: []
modified: ${new Date().toISOString()}
---
# Recent Conversation (Processing)
${conversation}`;
const [e] = await this.llm.embedding(content);
return {
name: `_temp_${timestamp}`,
description: 'Temporary memory - processing in background',
content,
embedding: e?.embedding || [],
};
}
forget(name: string, memories: Memory[] | MemoryCache): boolean {
const mem = memories instanceof MemoryCache ? memories.memories : memories;
const idx = mem.findIndex(m => m.name === name);
if (idx === -1) return false;
for (const node of mem) {
const {links, backlinks} = extractMetadata(node.content);
const newBacklinks = backlinks.filter(b => b !== name);
const newLinks = links.filter(l => l !== name);
if (newBacklinks.length !== backlinks.length || newLinks.length !== links.length) {
node.content = this.updateFrontmatter(node.content, {
links: newLinks,
backlinks: newBacklinks,
});
}
}
mem.splice(idx, 1);
if (memories instanceof MemoryCache) memories.rebuild();
return true;
}
private cosineSearch(query: number[], memories: Memory[], limit: number): MemoryRef[] {
const scored = memories
.filter(m => m.embedding?.length)
.map(m => ({
ref: {name: m.name, description: m.description},
distance: cosineDistance(query, m.embedding),
}))
.sort((a, b) => a.distance - b.distance)
.slice(0, limit);
return scored.map(s => s.ref);
}
private listNodes(memories: Memory[]): MemoryRef[] {
return memories.map(m => ({name: m.name, description: m.description}));
}
async recollect(query: string, memories: Memory[] | MemoryCache, limit = 5, graphDepth = 1): Promise<Memory[]> {
const mem: Memory[] = memories instanceof MemoryCache ? memories.memories : memories;
if (!mem.length) return [];
const [e] = await this.llm.embedding(query);
if (!e) return [];
let vectorResults: MemoryRef[];
if (memories instanceof MemoryCache) vectorResults = memories.search(e.embedding, limit);
else vectorResults = this.cosineSearch(e.embedding, mem, limit);
const found = new Set<string>(vectorResults.map(r => r.name));
if (graphDepth > 0) {
const frontier = [...found];
for (let depth = 0; depth < graphDepth; depth++) {
const next: string[] = [];
for (const name of frontier) {
const node = mem.find(m => m.name === name);
if (!node) continue;
const {links} = extractMetadata(node.content);
for (const link of links) {
if (!found.has(link) && mem.find(m => m.name === link)) {
found.add(link);
next.push(link);
}
}
}
frontier.splice(0, frontier.length, ...next);
if (!frontier.length) break;
}
}
const vectorOrder = vectorResults.map(r => r.name);
const graphExpansions = [...found].filter(n => !vectorOrder.includes(n));
const ordered = [...vectorOrder, ...graphExpansions];
return ordered.map(n => mem.find(m => m.name === n)!).filter(Boolean);
}
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}]: ${h.content}`).join('\n\n').trim();
if(!conversation) return [];
const trackingId = `${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();
this.pendingMemorizations.set(trackingId, {
memories,
tempMemoryName: tempMemory.name,
timestamp: Date.now(),
});
try {
await this._memorizeBackground(conversation, memories, options, tempMemory.name);
const finalMem = memories instanceof MemoryCache ? memories.memories : memories;
return finalMem.filter(m => !m.name.startsWith('_temp_'));
} finally {
const pending = this.pendingMemorizations.get(trackingId);
if (pending) {
const cleanMem = pending.memories instanceof MemoryCache
? pending.memories.memories
: pending.memories;
const idx = cleanMem.findIndex(m => m.name === pending.tempMemoryName);
if (idx !== -1) cleanMem.splice(idx, 1);
if (pending.memories instanceof MemoryCache) pending.memories.rebuild();
}
this.pendingMemorizations.delete(trackingId);
}
}
private async _memorizeBackground(conversation: string, memories: Memory[] | MemoryCache, options: LLMRequest, tempName: string): Promise<void> {
const mem = memories instanceof MemoryCache ? memories.memories : memories;
const monday = getWeekMonday();
const sunday = getWeekSunday(monday);
const buckets = await this.factAgent(conversation, mem, options, monday);
if(!buckets.length) return;
const jobs = [...buckets].map(({subject, facts}) => {
let node = mem.find(m => m.name === subject);
if(!node) {
node = {name: subject, description: '', content: '', embedding: [],};
mem.push(node);
}
const week = subject.startsWith('Journal/') ? {monday, sunday} : undefined;
return this.enqueue(node, facts, mem, options, tempName, week);
});
await Promise.all(jobs);
}
/**
* Coalescing queue: if a doc is already compiling, abort the in-flight run, merge its
* facts with the new ones and restart. Never blocks a pending update, never drops facts.
*/
private enqueue(node: Memory, facts: string[], memories: Memory[] | MemoryCache, options: LLMRequest, tempName: string, week?: {monday: string, sunday: string}): Promise<void> {
const key = node.name;
const existing = this.queues.get(key);
if (existing) {
existing.pending.push(...facts);
existing.request?.abort?.();
return existing.task;
}
const entry: {pending: string[], request: {abort?: () => void} | null, task: Promise<void>} = {pending: [...facts], request: null, task: Promise.resolve()};
this.queues.set(key, entry);
const m = memories instanceof MemoryCache ? memories.memories : memories;
entry.task = (async () => {
while (entry.pending.length) {
const batch = dedupeFacts(entry.pending.splice(0, entry.pending.length));
const written = await this.docAgent(node, batch, m, options, tempName, week, entry);
if (!written) entry.pending.unshift(...batch);
}
})().finally(() => {
this.queues.delete(key);
if(!this.queues.size && memories instanceof MemoryCache) memories.rebuild();
});
return entry.task;
}
private buildHeader(node: Memory, week?: {monday: string, sunday: string}, links: string[] = [], backlinks: string[] = []): string {
const tags = node.name.split('/')[0]?.toLowerCase();
const lines = [
'---',
`name: ${node.name}`,
`description: ${node.description || ''}`,
tags ? `tags: [${tags}]` : '',
links.length ? `links: [${links.map(l => `"${l}"`).join(', ')}]` : 'links: []',
backlinks.length ? `backlinks: [${backlinks.map(l => `"${l}"`).join(', ')}]` : 'backlinks: []',
week ? `week: ${week.monday} ${week.sunday}` : '',
`modified: ${new Date().toISOString()}`,
'---',
].filter(Boolean);
return lines.join('\n');
}
private applyHeader(content: string, header: string): string {
return `${header}\n\n${this.stripHeader(content)}`;
}
private updateFrontmatter(content: string, updates: {links?: string[], backlinks?: string[]}): string {
const match = content.match(/^---\n([\s\S]*?)\n---\n\n?([\s\S]*)$/);
if (!match) return content;
const [, fm, body] = match;
let newFm = fm;
if (updates.links !== undefined) {
const linksList = updates.links.length ? `[${updates.links.map(l => `"${l}"`).join(', ')}]` : '[]';
newFm = newFm.replace(/^links:.*$/m, `links: ${linksList}`);
}
if (updates.backlinks !== undefined) {
const backlinksList = updates.backlinks.length ? `[${updates.backlinks.map(l => `"${l}"`).join(', ')}]` : '[]';
newFm = newFm.replace(/^backlinks:.*$/m, `backlinks: ${backlinksList}`);
}
newFm = newFm.replace(/^modified:.*$/m, `modified: ${new Date().toISOString()}`);
return `---\n${newFm}\n---\n\n${body}`;
}
private stripHeader(content: string): string {
return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
}
private async docAgent(node: Memory, facts: string[], memories: Memory[], options: LLMRequest, tempName: string, week: {monday: string, sunday: string} | undefined, entry: {request: {abort?: () => void} | null}): Promise<boolean> {
const {links: oldLinks} = extractMetadata(node.content);
const currentBody = this.stripHeader(node.content);
let update;
try {
for(let i = 0; i < 3 && !update?.content; i++) {
const request = this.llm.ask(`New Facts:\n${facts.map(f => `- ${f}`).join('\n')}`, {
model: options.model,
temperature: 0.3,
schema: {
description: {type: 'string', description: 'One-line description of what this document covers, no formatting or emojis', required: true},
content: {type: 'string', description: 'Rewritten document in markdown, without the frontmatter block', required: true},
},
system: `You are a knowledge base editor. Rewrite the current document below so it incorporates the new facts.
Formatting rules:
- Use Obsidian-style markdown: # headings, **bold** to add emphasis, __italics__ for titles, terms, etc, bullet & numbered lists for grouped 1D data and tables for 2D data
- Link related concepts with [[WikiLink]] notation using full paths like [[People/Sarah]] or [[Projects/Website]]
- Create links for specific entities (person, place, project, program) and abstract concepts (quantum mechanics, entropy) but skip generics (car, red, dog)
- Keep the document concise, factual, and human-readable
- Resolve contradictions: the new facts always win — delete the outdated statement entirely, never keep both
- Later facts in the list override earlier ones
- Do not add frontmatter blocks, filler, preamble, or AI commentary
${week ? '- This is a weekly journal entry.\n' : ''}
All nodes:
${this.listNodes(memories).map(n => n.name).join(', ') || 'none'}
Current document:
\`\`\`markdown
${currentBody}
\`\`\``}
);
entry.request = request;
update = await request;
}
} catch (err: any) {
if (err?.name === 'AbortError') return false;
throw err;
} finally {
entry.request = null;
}
if(!update?.content) return false;
const newLinks = extractLinks(update.content).filter(l => l !== node.name && l !== tempName);
const newLinkSet = new Set(newLinks);
const oldLinkSet = new Set(oldLinks);
for (const added of newLinkSet) {
if (!oldLinkSet.has(added)) {
const target = memories.find(m => m.name === added);
if (target) {
const {backlinks} = extractMetadata(target.content);
if (!backlinks.includes(node.name)) {
target.content = this.updateFrontmatter(target.content, {
backlinks: [...backlinks, node.name],
});
}
}
}
}
for (const removed of oldLinkSet) {
if (!newLinkSet.has(removed)) {
const target = memories.find(m => m.name === removed);
if (target) {
const {backlinks} = extractMetadata(target.content);
target.content = this.updateFrontmatter(target.content, {
backlinks: backlinks.filter(b => b !== node.name),
});
}
}
}
const {backlinks} = extractMetadata(node.content);
node.description = node.name !== 'Person/User' ? update.description : 'All information about the current user';
node.content = this.applyHeader(update.content, this.buildHeader(node, week, newLinks, backlinks));
const [e] = await this.llm.embedding(node.content);
if(e) node.embedding = e.embedding;
return true;
}
private async factAgent(conversation: string, memories: Memory[], options: LLMRequest, weekKey: string): Promise<FactBucket[]> {
const buckets = new Map<string, string[]>();
await this.llm.ask(conversation, {
model: options.model,
temperature: 0.2,
system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long-term.
Rules:
- ONLY extract current facts the USER explicitly stated about themselves, their work, or their projects
- ONLY extract decisions that were MADE during this conversation
- DO NOT extract anything the AI said, its capabilities, or meta-conversation about the AI
- DO NOT extract greetings, pleasantries, or generic exchanges
- DO NOT extract deltas or changes in facts; ONLY the end fact
- If nothing worth remembering was said, do not call any tools
When extracting facts, you MUST also decide the exact destination path:
- Use an existing node name if the facts clearly belong there
- All information primary about the user should go under "People/User"
- When required, create a new path following collection/subject format (e.g., People/Sarah, Projects/Oxide)
- For journal entries, use "Journal"
Available nodes:
- Journal
${this.listNodes(memories).filter(n => !n.name.includes('_temp_') && !n.name.includes('Journal')).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}`,
tools: [{
name: 'facts_extract',
description: 'Submit facts with their destination',
args: {
destination: {type: 'string', description: 'Exact existing node name OR new path (e.g. "People/Sarah", "Projects/Oxide")', required: true},
facts: {type: 'string', description: 'Comma-separated facts', required: true},
},
fn: (args: any) => {
const subject = args.destination.trim().toLowerCase() === 'journal'
? `Journal/${weekKey}` : args.destination.trim();
const facts = buckets.get(subject) ?? [];
facts.push(...dedupeFacts(String(args.facts).split(',')));
buckets.set(subject, facts);
return 'Recorded';
},
}],
});
return buckets.entries().toArray().map(([subject, facts]) => ({subject, facts}));
}
}