More memory fixes
This commit is contained in:
@@ -3,6 +3,8 @@ export * from './antrhopic';
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export * from './audio';
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export * from './llm';
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export * from './memory';
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export * from './memory-cache';
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export * from './memory-graph';
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export * from './open-ai';
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export * from './provider';
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export * from './tools';
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52
src/llm.ts
52
src/llm.ts
@@ -1,12 +1,13 @@
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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-cache.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 {dirname, join} from 'path';
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import {spawn} from 'node:child_process';
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import {Memory, MemoryCache, MemoryManager} from './memory.ts';
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import {Memory, MemoryManager} from './memory.ts';
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export type AnthropicConfig = {proto: 'anthropic', token: string};
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export type OpenAiConfig = {proto: 'openai', host?: string, token: string};
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@@ -143,7 +144,7 @@ class LLM {
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return {
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prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
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tools: [{
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name: 'read_skill',
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name: 'skill_read',
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description: 'Read the full content of a skill/knowledge document',
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args: {
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name: {type: 'string', description: 'Exact skill name', required: true}
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@@ -167,8 +168,14 @@ class LLM {
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}
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const m = options.model || this.defaultModel;
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if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
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let abort = () => {};
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return Object.assign(new Promise<string>(async res => {
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let request: AbortablePromise<string> | null = null;
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let aborted = false;
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const abort = () => {
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aborted = true;
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request?.abort?.();
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};
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const promise = (async () => {
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let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
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const prompts: string[] = [];
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let history = options.history || [];
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@@ -192,22 +199,27 @@ class LLM {
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// Memory
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if (options.memory) {
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const mems = options.memory instanceof MemoryCache ? options.memory.memories : options.memory;
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const relevant = await this.memoryManager.recollect(message, options.memory, 5);
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prompts.unshift(`You have access to the following memory files:
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${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}
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${relevant.length ? `
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Relevant memories have been preloaded:
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${relevant.map(r => `
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**${r.name}**
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${r.description}
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${r.content}
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`).join('\n---\n')}
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` : ''}`.trim());
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tools.push(this.memoryManager.tools.read(options.memory));
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if(mems.length) {
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const relevant = await this.memoryManager.recollect(message, options.memory, 5);
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prompts.unshift(`You have access to the following memory files:
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${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}
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${relevant.length ? `
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Relevant memories have been preloaded:
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${relevant.map(r => `
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**${r.name}**
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${r.description}
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${r.content}
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`).join('\n---\n')}
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` : ''}`.trim());
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tools.push(this.memoryManager.tools.read(options.memory));
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}
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}
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if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
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prompts.unshift(options.system || this.ai.options.llm?.system || '');
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const resp = await this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
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request = this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
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const resp = await request;
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// Trim memory injections from history
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if(options.memory) {
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@@ -221,8 +233,10 @@ ${r.content}
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if(options.history) options.history.splice(0, options.history.length, ...compressed);
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}
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return res(resp);
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}), {abort});
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return resp;
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})();
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return Object.assign(promise, {abort});
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}
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/**
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59
src/memory-cache.ts
Normal file
59
src/memory-cache.ts
Normal file
@@ -0,0 +1,59 @@
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import {KDPoint, KDTree} from './kd-tree.ts';
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import {Memory, MemoryRef} from './memory.ts';
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export class MemoryCache {
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private tree: KDTree<MemoryRef>;
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public memories: Memory[];
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get length() { return this.memories.length; }
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constructor(memories: Memory[]) {
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this.memories = memories;
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this.tree = this.buildTree();
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}
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private buildTree(): KDTree<MemoryRef> {
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const embedded = this.memories.filter(m => m.embedding?.length);
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if (!embedded.length) return new KDTree<MemoryRef>(0);
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const dims = embedded[0].embedding.length;
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const points: KDPoint<MemoryRef>[] = embedded.map(m => ({
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vector: m.embedding,
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payload: {name: m.name, description: m.description},
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}));
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return new KDTree<MemoryRef>(dims, 'cosine', points);
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}
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search(query: number[], limit: number): MemoryRef[] {
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const results = this.tree.knn(query, limit);
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return results.map(r => r.point.payload);
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}
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add(memory: Memory): void {
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this.memories.push(memory);
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this.rebuild();
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}
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update(memory: Memory): void {
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const idx = this.memories.findIndex(m => m.name === memory.name);
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if (idx !== -1) {
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this.memories[idx] = memory;
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} else {
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this.memories.push(memory);
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}
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this.rebuild();
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}
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remove(name: string): void {
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const idx = this.memories.findIndex(m => m.name === name);
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if (idx !== -1) {
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this.memories.splice(idx, 1);
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this.rebuild();
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}
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}
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rebuild(): void {
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this.tree = this.buildTree();
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}
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}
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69
src/memory-graph.ts
Normal file
69
src/memory-graph.ts
Normal file
@@ -0,0 +1,69 @@
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import {MemoryCache} from './memory-cache.ts';
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import {extractMetadata, Memory, MemoryNode} from './memory.ts';
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export function buildMemoryGraph(memories: Memory[] | MemoryCache): MemoryNode[] {
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const mems = memories instanceof MemoryCache ? memories.memories : memories;
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const nameSet = new Set(mems.map(m => m.name));
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const ghosts = new Set<string>();
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const nodes: MemoryNode[] = mems.map(m => {
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const {links, backlinks} = extractMetadata(m.content);
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return {
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name: m.name,
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missing: false,
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links,
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backlinks,
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};
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});
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for (const node of nodes) {
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for (const link of node.links) {
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if (!nameSet.has(link)) ghosts.add(link);
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}
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}
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return [
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...nodes,
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...[...ghosts].map(name => ({
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name,
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missing: true,
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links: [],
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backlinks: nodes
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.filter(n => n.links.includes(name))
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.map(n => n.name),
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}))
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];
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}
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export function renderMemoryGraph(nodes) {
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if (!nodes.length) return 'No memories yet.';
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const groups = new Map();
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for (const node of nodes) {
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const [prefix, ...rest] = node.name.split('/');
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const group = rest.length ? prefix : 'Root';
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const label = rest.length ? rest.join('/') : node.name;
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if (!groups.has(group)) groups.set(group, []);
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groups.get(group).push({...node, label});
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}
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const ghostCount = nodes.filter(n => n.missing).length;
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const lines = [`Memory Graph (${nodes.length} nodes, ${ghostCount} ghost${ghostCount === 1 ? '' : 's'})`, ''];
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for (const group of [...groups.keys()].sort()) {
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const items = groups.get(group).sort((a, b) => a.label.localeCompare(b.label));
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lines.push(`${group}/`);
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items.forEach((n, i) => {
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const last = i === items.length - 1;
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const branch = last ? '└─' : '├─';
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const pad = last ? ' ' : '│ ';
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const tag = n.missing ? ' (ghost)' : '';
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lines.push(` ${branch} ${n.label}${tag}`);
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if (n.links.length) lines.push(` ${pad} → ${n.links.join(', ')}`);
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if (n.backlinks.length) lines.push(` ${pad} ← ${n.backlinks.join(', ')}`);
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});
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lines.push('');
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}
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return lines.join('\n').trimEnd();
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}
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374
src/memory.ts
374
src/memory.ts
@@ -1,6 +1,6 @@
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import {LLMRequest, LLMMessage} from './llm.ts';
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import {MemoryCache} from './memory-cache.ts';
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import {AiTool} from './tools.ts';
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import {KDTree, KDPoint} from './kd-tree.ts';
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export type Memory = {
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name: string;
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@@ -9,15 +9,14 @@ export type Memory = {
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embedding: number[];
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}
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type MemoryRef = {
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export type MemoryRef = {
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name: string;
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description: string;
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}
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type FactBucket = {
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export type FactBucket = {
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subject: string;
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facts: string[];
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isNew: boolean;
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}
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export type MemoryNode = {
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@@ -27,41 +26,8 @@ export type MemoryNode = {
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backlinks: string[];
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}
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export function buildMemoryGraph(memories: Memory[] | MemoryCache): MemoryNode[] {
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const mems = memories instanceof MemoryCache ? memories.memories : memories;
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const nameSet = new Set(mems.map(m => m.name));
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const ghosts = new Set<string>();
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const nodes: MemoryNode[] = mems.map(m => {
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const {links, backlinks} = extractMetadata(m.content);
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return {
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name: m.name,
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missing: false,
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links,
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backlinks,
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};
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});
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for (const node of nodes) {
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for (const link of node.links) {
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if (!nameSet.has(link)) ghosts.add(link);
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}
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}
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return [
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...nodes,
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...[...ghosts].map(name => ({
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name,
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missing: true,
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links: [],
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backlinks: nodes
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.filter(n => n.links.includes(name))
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.map(n => n.name),
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}))
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];
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}
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function extractLinks(content: string): string[] {
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if(!content) return [];
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const matches = content.matchAll(/\[\[([^\]]+)\]\]/g);
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return [...new Set([...matches].map(m => m[1].trim()))];
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}
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@@ -83,6 +49,15 @@ export function extractMetadata(content: string): {links: string[], backlinks: s
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};
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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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function cosineDistance(a: number[], b: number[]): number {
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let dot = 0, normA = 0, normB = 0;
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for (let i = 0; i < a.length; i++) {
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@@ -108,103 +83,7 @@ function getWeekSunday(monday: string): string {
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return d.toISOString().slice(0, 10);
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}
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function tagsFromName(name: string): string[] {
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const prefix = name.split('/')[0];
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return prefix ? [prefix.toLowerCase()] : [];
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}
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export function serializeMemory(mem: Memory, week?: {monday: string, sunday: string}): string {
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return mem.content;
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}
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export function deserializeMemory(raw: string, embedding: number[] = []): Memory {
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const match = raw.match(/^---\n([\s\S]*?)\n---\n\n?([\s\S]*)$/);
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if (!match) {
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return {name: '', description: '', content: raw.trim(), embedding};
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}
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const [, fm] = match;
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const get = (key: string): string => {
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const m = fm.match(new RegExp(`^${key}:\\s*(.+)$`, 'm'));
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return m ? m[1].trim() : '';
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};
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return {
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name: get('name'),
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description: get('description'),
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content: raw.trim(),
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embedding,
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};
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}
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export class MemoryCache {
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private tree: KDTree<MemoryRef>;
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public memories: Memory[];
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private locks = new Map<string, Promise<void>>();
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constructor(memories: Memory[]) {
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this.memories = memories;
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this.tree = this.buildTree();
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}
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private buildTree(): KDTree<MemoryRef> {
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const embedded = this.memories.filter(m => m.embedding?.length);
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if (!embedded.length) return new KDTree<MemoryRef>(0);
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const dims = embedded[0].embedding.length;
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const points: KDPoint<MemoryRef>[] = embedded.map(m => ({
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vector: m.embedding,
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payload: {name: m.name, description: m.description},
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}));
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return new KDTree<MemoryRef>(dims, 'cosine', points);
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}
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search(query: number[], limit: number): MemoryRef[] {
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const results = this.tree.knn(query, limit);
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return results.map(r => r.point.payload);
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}
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add(memory: Memory): void {
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this.memories.push(memory);
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this.rebuild();
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}
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update(memory: Memory): void {
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const idx = this.memories.findIndex(m => m.name === memory.name);
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if (idx !== -1) {
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this.memories[idx] = memory;
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} else {
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this.memories.push(memory);
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}
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this.rebuild();
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}
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remove(name: string): void {
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const idx = this.memories.findIndex(m => m.name === name);
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if (idx !== -1) {
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this.memories.splice(idx, 1);
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this.rebuild();
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}
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}
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rebuild(): void {
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this.tree = this.buildTree();
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}
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lock<T>(name: string, fn: () => Promise<T>): Promise<T> {
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const prev = this.locks.get(name) ?? Promise.resolve();
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let resolveLock!: () => void;
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const next = new Promise<void>(r => { resolveLock = r; });
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this.locks.set(name, next);
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const result = prev.then(fn).finally(resolveLock);
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result.finally(() => {
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if (this.locks.get(name) === next) this.locks.delete(name);
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});
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return result;
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}
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}
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export class MemoryManager {
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private pendingMemorizations = new Map<string, {
|
||||
@@ -213,9 +92,15 @@ export class MemoryManager {
|
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timestamp: number,
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}>();
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|
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private queues = new Map<string, {
|
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pending: string[],
|
||||
request: {abort?: () => void} | null,
|
||||
task: Promise<void>,
|
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}>();
|
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tools = {
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read: (memories: Memory[] | MemoryCache): AiTool => ({
|
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name: 'read_memory',
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name: 'memory_recall',
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description: 'Read the full content of a memory document',
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args: {
|
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name: {type: 'string', description: 'Exact memory name', required: true},
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@@ -229,11 +114,10 @@ export class MemoryManager {
|
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}),
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forget: (memories: Memory[] | MemoryCache): AiTool => ({
|
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name: 'forget_memory',
|
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name: 'memory_forget',
|
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description: 'Permanently delete a memory document and clean up all references to it',
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args: {
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name: {type: 'string', description: 'Exact memory name to forget', required: true},
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reason: {type: 'string', description: 'Why this memory is being deleted', required: true},
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name: {type: 'string', description: 'Exact memory name to forget', required: true}
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},
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||||
fn: (args: any) => {
|
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const result = this.forget(args.name, memories);
|
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@@ -245,12 +129,8 @@ export class MemoryManager {
|
||||
constructor(private llm: any) {}
|
||||
|
||||
private async createTempMemory(conversation: string): Promise<Memory> {
|
||||
const [e] = await this.llm.embedding(conversation);
|
||||
const timestamp = Date.now();
|
||||
return {
|
||||
name: `_temp_${timestamp}`,
|
||||
description: 'Temporary memory - processing in background',
|
||||
content: `---
|
||||
const content = `---
|
||||
name: _temp_${timestamp}
|
||||
description: Temporary memory - processing in background
|
||||
tags: [_temporary]
|
||||
@@ -261,7 +141,12 @@ modified: ${new Date().toISOString()}
|
||||
|
||||
# Recent Conversation (Processing)
|
||||
|
||||
${conversation}`,
|
||||
${conversation}`;
|
||||
const [e] = await this.llm.embedding(content);
|
||||
return {
|
||||
name: `_temp_${timestamp}`,
|
||||
description: 'Temporary memory - processing in background',
|
||||
content,
|
||||
embedding: e?.embedding || [],
|
||||
};
|
||||
}
|
||||
@@ -302,17 +187,6 @@ ${conversation}`,
|
||||
return scored.map(s => s.ref);
|
||||
}
|
||||
|
||||
private createNode(name: string, memories: Memory[]): Memory {
|
||||
const existing = memories.find(m => m.name === name);
|
||||
if (existing) return existing;
|
||||
return {
|
||||
name,
|
||||
description: '',
|
||||
content: '',
|
||||
embedding: [],
|
||||
};
|
||||
}
|
||||
|
||||
private listNodes(memories: Memory[]): MemoryRef[] {
|
||||
return memories.map(m => ({name: m.name, description: m.description}));
|
||||
}
|
||||
@@ -361,7 +235,6 @@ ${conversation}`,
|
||||
.map(h => `[${h.role}]: ${h.content}`).join('\n\n').trim();
|
||||
if(!conversation) return [];
|
||||
|
||||
// Create and insert temp memory immediately
|
||||
const trackingId = `${Date.now()}_${Math.random()}`;
|
||||
const tempMemory = await this.createTempMemory(conversation);
|
||||
const mem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
@@ -374,65 +247,68 @@ ${conversation}`,
|
||||
});
|
||||
|
||||
try {
|
||||
await this._memorizeBackground(conversation, memories, options);
|
||||
// Return the final memories (excluding temp ones)
|
||||
await this._memorizeBackground(conversation, memories, options, tempMemory.name);
|
||||
const finalMem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
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) {
|
||||
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();
|
||||
}
|
||||
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): Promise<void> {
|
||||
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 runDocAgent = (node: Memory, bucket: FactBucket, embedding?: number[], week?: {monday: string, sunday: string}) => {
|
||||
if (memories instanceof MemoryCache) {
|
||||
return memories.lock(node.name, () => this.docAgent(node, bucket, mem, options, embedding, week));
|
||||
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);
|
||||
}
|
||||
return this.docAgent(node, bucket, mem, options, embedding, week);
|
||||
};
|
||||
const week = subject.startsWith('Journal/') ? {monday, sunday} : undefined;
|
||||
return this.enqueue(node, facts, mem, options, tempName, week);
|
||||
});
|
||||
await Promise.all(jobs);
|
||||
}
|
||||
|
||||
await Promise.all(buckets.map(async bucket => {
|
||||
let node = mem.find(m => m.name === bucket.subject && !m.name.startsWith('_temp_'));
|
||||
let embedding: number[] | undefined;
|
||||
/**
|
||||
* 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;
|
||||
}
|
||||
|
||||
if (!node || bucket.isNew) {
|
||||
const [e] = await this.llm.embedding(`${bucket.subject}\n${bucket.facts.join('\n')}`);
|
||||
embedding = e?.embedding;
|
||||
|
||||
if (!node) {
|
||||
node = this.createNode(bucket.subject, mem);
|
||||
mem.push(node);
|
||||
}
|
||||
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);
|
||||
}
|
||||
|
||||
const week = bucket.subject.startsWith('Journal/') ? {monday, sunday} : undefined;
|
||||
await runDocAgent(node, bucket, embedding, week);
|
||||
}));
|
||||
|
||||
if(memories instanceof MemoryCache)
|
||||
memories.rebuild();
|
||||
})().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 {
|
||||
@@ -452,11 +328,7 @@ ${conversation}`,
|
||||
}
|
||||
|
||||
private applyHeader(content: string, header: string): string {
|
||||
const hasFrontmatter = content.trimStart().startsWith('---');
|
||||
if (hasFrontmatter) {
|
||||
return content.replace(/^---[\s\S]*?---\n?/, `${header}\n`);
|
||||
}
|
||||
return `${header}\n\n${content}`;
|
||||
return `${header}\n\n${this.stripHeader(content)}`;
|
||||
}
|
||||
|
||||
private updateFrontmatter(content: string, updates: {links?: string[], backlinks?: string[]}): string {
|
||||
@@ -482,53 +354,53 @@ ${conversation}`,
|
||||
}
|
||||
|
||||
private stripHeader(content: string): string {
|
||||
return content.replace(/^---[\s\S]*?---\n?/, '').trimStart();
|
||||
return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
|
||||
}
|
||||
|
||||
private async docAgent(node: Memory, bucket: FactBucket, memories: Memory[], options: LLMRequest, precomputedEmbedding?: number[], week?: {monday: string, sunday: string}): Promise<void> {
|
||||
private async docAgent(node: Memory, facts: string[], memories: Memory[], options: LLMRequest, tempName: string, week: {monday: string, sunday: string} | undefined, entry: {request: {abort?: () => void} | null}): Promise<boolean> {
|
||||
const {links: oldLinks} = extractMetadata(node.content);
|
||||
let finalContent = node.content;
|
||||
|
||||
await this.llm.ask(
|
||||
`New facts to integrate:\n${bucket.facts.map(f => `- ${f}`).join('\n')}`,
|
||||
{
|
||||
model: options.model,
|
||||
temperature: 0.3,
|
||||
system: `You are a knowledge base editor. Integrate the provided facts into the document below.
|
||||
const currentBody = this.stripHeader(node.content);
|
||||
let update;
|
||||
try {
|
||||
for(let i = 0; i < 3 && !update?.content; i++) {
|
||||
const request = this.llm.ask(`New Facts:\n${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** for key terms, bullet lists for facts
|
||||
- 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]]
|
||||
- You may create links to nodes that don't exist yet if the concept is important
|
||||
- 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 any contradictions between old content and new facts (new facts win)
|
||||
- Do not add filler, preamble, or AI commentary — just clean knowledge documents
|
||||
- The document begins with a YAML frontmatter block (between --- markers) — do not remove or rewrite it, it is maintained automatically
|
||||
${week ? '- This is a weekly journal entry. The frontmatter contains the week date range.\n' : ''}
|
||||
- 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
|
||||
${node.content || '(empty — this is a new document)'}
|
||||
\`\`\``,
|
||||
tools: [{
|
||||
name: 'update_document',
|
||||
description: 'Write the complete updated document content. Include everything after the frontmatter block — the frontmatter will be recalculated automatically.',
|
||||
args: {
|
||||
description: {type: 'string', description: 'One-line description of what this document covers, no formatting or emojis', required: true},
|
||||
content: {type: 'string', description: 'Document body in markdown, without the frontmatter block', required: true},
|
||||
},
|
||||
fn: (args: any) => {
|
||||
node.description = args.description;
|
||||
finalContent = args.content;
|
||||
return 'Saved';
|
||||
},
|
||||
}],
|
||||
${currentBody}
|
||||
\`\`\``}
|
||||
);
|
||||
entry.request = request;
|
||||
update = await request;
|
||||
}
|
||||
);
|
||||
} catch (err: any) {
|
||||
if (err?.name === 'AbortError') return false;
|
||||
throw err;
|
||||
} finally {
|
||||
entry.request = null;
|
||||
}
|
||||
|
||||
const newLinks = extractLinks(finalContent).filter(l => l !== node.name);
|
||||
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);
|
||||
|
||||
@@ -558,61 +430,55 @@ ${node.content || '(empty — this is a new document)'}
|
||||
}
|
||||
|
||||
const {backlinks} = extractMetadata(node.content);
|
||||
const header = this.buildHeader(node, week, newLinks, backlinks);
|
||||
node.content = this.applyHeader(finalContent, header);
|
||||
|
||||
if (precomputedEmbedding) {
|
||||
node.embedding = precomputedEmbedding;
|
||||
} else {
|
||||
const embedInput = `${node.description}\n\n${this.stripHeader(node.content)}`.trim();
|
||||
const [e] = await this.llm.embedding(embedInput);
|
||||
if (e) node.embedding = e.embedding;
|
||||
}
|
||||
node.description = update.description;
|
||||
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: 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 facts the USER explicitly stated about themselves, their work, or their projects
|
||||
- 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 "Personal/Subject" (e.g., Personal/Info, Personal/Todos)
|
||||
- All information primary about the user should go under "Personal/..." (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})
|
||||
- For journal entries, use "Journal"
|
||||
|
||||
Available nodes:
|
||||
${this.listNodes(memories).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}`,
|
||||
- Journal
|
||||
${this.listNodes(memories).filter(n => !n.name.includes('_temp_') && !n.name.includes('Journal')).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}`,
|
||||
tools: [{
|
||||
name: 'extract_facts',
|
||||
name: 'facts_extract',
|
||||
description: 'Submit facts with their destination',
|
||||
args: {
|
||||
destination: {type: 'string', description: 'Exact existing node name OR new path (e.g. "People/Sarah", "Projects/Oxide")', required: true},
|
||||
facts: {type: 'string', description: 'Comma-separated facts', required: true},
|
||||
create_new: {type: 'boolean', description: 'True if this is a new node that doesn\'t exist yet', required: true},
|
||||
},
|
||||
fn: (args: any) => {
|
||||
const subject = args.destination.trim().toLowerCase() === 'journal'
|
||||
? `Journal/${weekKey}`
|
||||
: args.destination;
|
||||
buckets.push({
|
||||
subject,
|
||||
facts: args.facts.split(',').map((f: string) => f.trim()).filter(Boolean),
|
||||
isNew: args.create_new,
|
||||
});
|
||||
: args.destination.trim();
|
||||
const facts = buckets.get(subject) ?? [];
|
||||
facts.push(...dedupeFacts(String(args.facts).split(',')));
|
||||
buckets.set(subject, facts);
|
||||
return 'Recorded';
|
||||
},
|
||||
}],
|
||||
});
|
||||
return buckets;
|
||||
return buckets.entries().toArray().map(([subject, facts]) => ({subject, facts}));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import {AbortablePromise} from './ai.ts';
|
||||
import {LLMMessage, LLMRequest} from './llm.ts';
|
||||
import {LLMRequest} from './llm.ts';
|
||||
|
||||
export abstract class LLMProvider {
|
||||
abstract ask(message: string, options: LLMRequest): AbortablePromise<string>;
|
||||
|
||||
@@ -133,7 +133,7 @@ export const ExecTool: AiTool = {
|
||||
}
|
||||
|
||||
export const FetchTool: AiTool = {
|
||||
name: 'fetch',
|
||||
name: 'net_fetch',
|
||||
description: 'Make HTTP request to URL',
|
||||
args: {
|
||||
url: {type: 'string', description: 'URL to fetch', required: true},
|
||||
@@ -172,7 +172,7 @@ export const PythonTool: AiTool = {
|
||||
}
|
||||
|
||||
export const ReadWebpageTool: AiTool = {
|
||||
name: 'read_webpage',
|
||||
name: 'net_read',
|
||||
description: 'Extract clean content from webpages, or convert media/documents to accessible formats',
|
||||
args: {
|
||||
url: {type: 'string', description: 'URL to read', required: true},
|
||||
@@ -276,7 +276,7 @@ export const ReadWebpageTool: AiTool = {
|
||||
};
|
||||
|
||||
export const WebSearchTool: AiTool = {
|
||||
name: 'web_search',
|
||||
name: 'net_search',
|
||||
description: 'Use duckduckgo (anonymous) to find find relevant online resources. Returns a list of URLs that works great with the `read_webpage` tool',
|
||||
args: {
|
||||
query: {type: 'string', description: 'Search string', required: true},
|
||||
@@ -302,7 +302,7 @@ export const WebSearchTool: AiTool = {
|
||||
}
|
||||
|
||||
export const WikipediaTool: AiTool = {
|
||||
name: 'wikipedia_search',
|
||||
name: 'get_wikipedia',
|
||||
description: 'Search Wikipedia for matching articles',
|
||||
args: {
|
||||
query: {type: 'string', description: 'Search term or article title', required: true},
|
||||
|
||||
Reference in New Issue
Block a user