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| Author | SHA1 | Date | |
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ztimson
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3f1289d993 |
@@ -119,7 +119,7 @@ const ai = new Ai({
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system: 'You are a helpful assistant.',
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compress: {max: 90_000, min: 50_000}, // Compress chat history to min tokens when max is reached
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temperature: 0.8,
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max_tokens: 100_000,
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maxTokens: 100_000,
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memoryModel: 'gpt-4o', // Cheap model for managing memories in background, defaults to current model
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models: {
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'claude-3-5-sonnet': {proto: 'anthropic', token: process.env.ANTHROPIC_TOKEN},
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@@ -186,7 +186,7 @@ console.log(chunks);
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// Manually compile history into memories at end of conversation
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// Happens automatically when coverstaions are compressed
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await ai.language.updateMemory(history, memory);
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await ai.language.memorize(history, memory);
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// Summarize text
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const summary = await ai.language.summarize(longText, 200);
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Generated
+317
-209
@@ -1,21 +1,22 @@
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{
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"name": "@ztimson/ai-utils",
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"version": "1.2.6",
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"version": "1.6.6",
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"lockfileVersion": 3,
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"requires": true,
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"packages": {
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"": {
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"name": "@ztimson/ai-utils",
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"version": "1.2.6",
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"version": "1.6.6",
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"license": "MIT",
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"dependencies": {
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"@anthropic-ai/sdk": "^0.102.0",
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"@huggingface/transformers": "^4.2.0",
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"@tensorflow/tfjs": "^4.22.0",
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"@ztimson/node-utils": "^1.0.7",
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"@ztimson/utils": "^0.29.4",
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"@ztimson/utils": "^0.30.8",
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"cheerio": "^1.2.0",
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"openai": "^6.42.0",
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"pdf-parse": "^2.4.5",
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"tesseract.js": "^7.0.0"
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},
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"devDependencies": {
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@@ -56,39 +57,12 @@
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@@ -577,16 +551,6 @@
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"url": "https://opencollective.com/libvips"
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}
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},
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"node_modules/@img/sharp-wasm32/node_modules/@emnapi/runtime": {
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"version": "1.11.3",
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"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.3.tgz",
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@@ -1005,63 +1146,10 @@
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@@ -1317,17 +1405,6 @@
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@@ -1448,9 +1525,9 @@
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@@ -1544,9 +1621,9 @@
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@@ -3011,9 +3088,9 @@
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||||
"version": "3.3.18",
|
||||
"resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.18.tgz",
|
||||
"integrity": "sha512-DTg4MJbGMWkfi6VZFdNt2/caMbQy4Ou+Op/hJQvGEWcnVfoA1QA+xzRKAzw9jD6+GVOOeYr/mIcuDSdug6F6+w==",
|
||||
"dev": true,
|
||||
"funding": [
|
||||
{
|
||||
@@ -3233,6 +3310,38 @@
|
||||
"dev": true,
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/pdf-parse": {
|
||||
"version": "2.4.5",
|
||||
"resolved": "https://registry.npmjs.org/pdf-parse/-/pdf-parse-2.4.5.tgz",
|
||||
"integrity": "sha512-mHU89HGh7v+4u2ubfnevJ03lmPgQ5WU4CxAVmTSh/sxVTEDYd1er/dKS/A6vg77NX47KTEoihq8jZBLr8Cxuwg==",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"@napi-rs/canvas": "0.1.80",
|
||||
"pdfjs-dist": "5.4.296"
|
||||
},
|
||||
"bin": {
|
||||
"pdf-parse": "bin/cli.mjs"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=20.16.0 <21 || >=22.3.0"
|
||||
},
|
||||
"funding": {
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/mehmet-kozan"
|
||||
}
|
||||
},
|
||||
"node_modules/pdfjs-dist": {
|
||||
"version": "5.4.296",
|
||||
"resolved": "https://registry.npmjs.org/pdfjs-dist/-/pdfjs-dist-5.4.296.tgz",
|
||||
"integrity": "sha512-DlOzet0HO7OEnmUmB6wWGJrrdvbyJKftI1bhMitK7O2N8W2gc757yyYBbINy9IDafXAV9wmKr9t7xsTaNKRG5Q==",
|
||||
"license": "Apache-2.0",
|
||||
"engines": {
|
||||
"node": ">=20.16.0 || >=22.3.0"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@napi-rs/canvas": "^0.1.80"
|
||||
}
|
||||
},
|
||||
"node_modules/picocolors": {
|
||||
"version": "1.1.1",
|
||||
"resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz",
|
||||
@@ -3272,9 +3381,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/postcss": {
|
||||
"version": "8.5.25",
|
||||
"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.25.tgz",
|
||||
"integrity": "sha512-DTPx3RWSSnWyzLxQnlH0rJP+EW5ekl16ZU4/psbIhA0e53kJfdgaN5vKM+xP7yJtXVu+nfdVFmlgFDEKAe4Pyw==",
|
||||
"version": "8.5.26",
|
||||
"resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.26.tgz",
|
||||
"integrity": "sha512-u82N74LFzG8ca+dD8puPnplTXoGH4fTPpVGuIbt36G3qvNlkvfD0lEAZSxaly3KX8TS/L1A1gsCEmvKmBcVbkQ==",
|
||||
"dev": true,
|
||||
"funding": [
|
||||
{
|
||||
@@ -3292,7 +3401,7 @@
|
||||
],
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"nanoid": "^3.3.16",
|
||||
"nanoid": "^3.3.17",
|
||||
"picocolors": "^1.1.1",
|
||||
"source-map-js": "^1.2.1"
|
||||
},
|
||||
@@ -3434,13 +3543,13 @@
|
||||
"license": "BSD-3-Clause"
|
||||
},
|
||||
"node_modules/rolldown": {
|
||||
"version": "1.1.5",
|
||||
"resolved": "https://registry.npmjs.org/rolldown/-/rolldown-1.1.5.tgz",
|
||||
"integrity": "sha512-t9z29cJjXf/vxQ8dyhCSpt6H6aSwHTk8cT5I3iy6SMXuFpk5mB6PL6XfC8PCwrPTx93udwKUm9HRteAlTGBLiA==",
|
||||
"version": "1.2.4",
|
||||
"resolved": "https://registry.npmjs.org/rolldown/-/rolldown-1.2.4.tgz",
|
||||
"integrity": "sha512-rSr7irW0K7QRWzjdJXqZowkcRdDtjRduh43rBltnVKd0VFq839l1lJoDvGJb6gl7+4rTTCrPWu+YfujUL8Ug7w==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"@oxc-project/types": "=0.139.0",
|
||||
"@oxc-project/types": "=0.144.0",
|
||||
"@rolldown/pluginutils": "^1.0.0"
|
||||
},
|
||||
"bin": {
|
||||
@@ -3450,21 +3559,20 @@
|
||||
"node": "^20.19.0 || >=22.12.0"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@rolldown/binding-android-arm64": "1.1.5",
|
||||
"@rolldown/binding-darwin-arm64": "1.1.5",
|
||||
"@rolldown/binding-darwin-x64": "1.1.5",
|
||||
"@rolldown/binding-freebsd-x64": "1.1.5",
|
||||
"@rolldown/binding-linux-arm-gnueabihf": "1.1.5",
|
||||
"@rolldown/binding-linux-arm64-gnu": "1.1.5",
|
||||
"@rolldown/binding-linux-arm64-musl": "1.1.5",
|
||||
"@rolldown/binding-linux-ppc64-gnu": "1.1.5",
|
||||
"@rolldown/binding-linux-s390x-gnu": "1.1.5",
|
||||
"@rolldown/binding-linux-x64-gnu": "1.1.5",
|
||||
"@rolldown/binding-linux-x64-musl": "1.1.5",
|
||||
"@rolldown/binding-openharmony-arm64": "1.1.5",
|
||||
"@rolldown/binding-wasm32-wasi": "1.1.5",
|
||||
"@rolldown/binding-win32-arm64-msvc": "1.1.5",
|
||||
"@rolldown/binding-win32-x64-msvc": "1.1.5"
|
||||
"@rolldown/binding-android-arm64": "1.2.4",
|
||||
"@rolldown/binding-darwin-arm64": "1.2.4",
|
||||
"@rolldown/binding-darwin-x64": "1.2.4",
|
||||
"@rolldown/binding-freebsd-x64": "1.2.4",
|
||||
"@rolldown/binding-linux-arm-gnueabihf": "1.2.4",
|
||||
"@rolldown/binding-linux-arm64-gnu": "1.2.4",
|
||||
"@rolldown/binding-linux-arm64-musl": "1.2.4",
|
||||
"@rolldown/binding-linux-ppc64-gnu": "1.2.4",
|
||||
"@rolldown/binding-linux-s390x-gnu": "1.2.4",
|
||||
"@rolldown/binding-linux-x64-gnu": "1.2.4",
|
||||
"@rolldown/binding-linux-x64-musl": "1.2.4",
|
||||
"@rolldown/binding-openharmony-arm64": "1.2.4",
|
||||
"@rolldown/binding-win32-arm64-msvc": "1.2.4",
|
||||
"@rolldown/binding-win32-x64-msvc": "1.2.4"
|
||||
}
|
||||
},
|
||||
"node_modules/safe-buffer": {
|
||||
@@ -4021,16 +4129,16 @@
|
||||
}
|
||||
},
|
||||
"node_modules/vite": {
|
||||
"version": "8.1.5",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-8.1.5.tgz",
|
||||
"integrity": "sha512-7ULLwsCdYx/nRyrpiEwvqb5TFHrMVZyBt+rg/OAXT7rgj/z+DtTDyKFeLAdDkubDVDKD8jOsndmy7m55XcfUsw==",
|
||||
"version": "8.2.1",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-8.2.1.tgz",
|
||||
"integrity": "sha512-EU/eS7BH3XROHh2YnBefjM6DBKA6ZeMZEYQbj7NLWg5wHYlhB8B/Mayd5XsgWq+NFYccDOTemRpdETWR6Ka/lw==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"lightningcss": "^1.32.0",
|
||||
"lightningcss": "^1.33.0",
|
||||
"picomatch": "^4.0.5",
|
||||
"postcss": "^8.5.17",
|
||||
"rolldown": "~1.1.5",
|
||||
"postcss": "^8.5.25",
|
||||
"rolldown": "~1.2.1",
|
||||
"tinyglobby": "^0.2.17"
|
||||
},
|
||||
"bin": {
|
||||
@@ -4047,7 +4155,7 @@
|
||||
},
|
||||
"peerDependencies": {
|
||||
"@types/node": "^20.19.0 || >=22.12.0",
|
||||
"@vitejs/devtools": "^0.3.0",
|
||||
"@vitejs/devtools": "^0.4.0",
|
||||
"esbuild": "^0.27.0 || ^0.28.0",
|
||||
"jiti": ">=1.21.0",
|
||||
"less": "^4.0.0",
|
||||
@@ -4132,9 +4240,9 @@
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/wasm-feature-detect": {
|
||||
"version": "1.8.0",
|
||||
"resolved": "https://registry.npmjs.org/wasm-feature-detect/-/wasm-feature-detect-1.8.0.tgz",
|
||||
"integrity": "sha512-zksaLKM2fVlnB5jQQDqKXXwYHLQUVH9es+5TOOHwGOVJOCeRBCiPjwSg+3tN2AdTCzjgli4jijCH290kXb/zWQ==",
|
||||
"version": "1.9.0",
|
||||
"resolved": "https://registry.npmjs.org/wasm-feature-detect/-/wasm-feature-detect-1.9.0.tgz",
|
||||
"integrity": "sha512-zonE+xlIIYtxPy++L24ow0hAD8CICb4+FgPyROd3buyXIqsJvUEDkBgfCCoXOd1Hu3DUr0GOfnPIdcGV+YpNaA==",
|
||||
"license": "Apache-2.0"
|
||||
},
|
||||
"node_modules/webidl-conversions": {
|
||||
|
||||
+4
-3
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "@ztimson/ai-utils",
|
||||
"version": "1.4.2",
|
||||
"version": "1.7.2",
|
||||
"description": "AI Utility library",
|
||||
"author": "Zak Timson",
|
||||
"license": "MIT",
|
||||
@@ -26,12 +26,13 @@
|
||||
},
|
||||
"dependencies": {
|
||||
"@anthropic-ai/sdk": "^0.102.0",
|
||||
"@tensorflow/tfjs": "^4.22.0",
|
||||
"@huggingface/transformers": "^4.2.0",
|
||||
"@tensorflow/tfjs": "^4.22.0",
|
||||
"@ztimson/node-utils": "^1.0.7",
|
||||
"@ztimson/utils": "^0.29.4",
|
||||
"@ztimson/utils": "^0.30.8",
|
||||
"cheerio": "^1.2.0",
|
||||
"openai": "^6.42.0",
|
||||
"pdf-parse": "^2.4.5",
|
||||
"tesseract.js": "^7.0.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
|
||||
@@ -4,7 +4,7 @@ import { Audio } from './audio.ts';
|
||||
import {Vision} from './vision.ts';
|
||||
|
||||
export type AbortablePromise<T> = Promise<T> & {
|
||||
abort: () => any
|
||||
abort: (keep?: boolean) => any
|
||||
};
|
||||
|
||||
export type AiOptions = {
|
||||
|
||||
+9
-2
@@ -24,6 +24,13 @@ export class Anthropic extends LLMProvider {
|
||||
return client;
|
||||
}
|
||||
|
||||
private toWireContent(content: any): any {
|
||||
if(!Array.isArray(content)) return content;
|
||||
return content.map(c => c.type === 'image'
|
||||
? {type: 'image', source: {type: 'base64', media_type: c.mime, data: c.data}}
|
||||
: {type: 'text', text: c.text});
|
||||
}
|
||||
|
||||
/** Convert standard history -> Anthropic wire format */
|
||||
private toWire(history: LLMMessage[]): any[] {
|
||||
const wire: any[] = [];
|
||||
@@ -34,7 +41,7 @@ export class Anthropic extends LLMProvider {
|
||||
{role: 'user', content: [{type: 'tool_result', tool_use_id: h.id, is_error: !!h.error, content: h.error || h.content || ''}]}
|
||||
);
|
||||
} else {
|
||||
wire.push({role: h.role, content: h.content});
|
||||
wire.push({role: h.role, content: this.toWireContent(h.content)});
|
||||
}
|
||||
}
|
||||
return wire;
|
||||
@@ -50,7 +57,7 @@ export class Anthropic extends LLMProvider {
|
||||
const tools = options.tools || this.ai.options.llm?.tools || [];
|
||||
const requestParams: any = {
|
||||
model: options.model || this.model,
|
||||
max_tokens: options.max_tokens || this.ai.options.llm?.max_tokens || 4096,
|
||||
max_tokens: options.maxTokens || this.ai.options.llm?.maxTokens || 4096,
|
||||
system: options.system || this.ai.options.llm?.system || '',
|
||||
temperature: options.temperature || this.ai.options.llm?.temperature || undefined,
|
||||
tools: tools.map(t => ({
|
||||
|
||||
@@ -1,72 +0,0 @@
|
||||
import {Memory, MemoryCache} from './memory.ts';
|
||||
|
||||
export type MemoryNode = {
|
||||
name: string;
|
||||
missing: boolean;
|
||||
links: string[];
|
||||
backlinks: string[];
|
||||
}
|
||||
|
||||
export function buildMemoryGraph(memories: Memory[] | MemoryCache): MemoryNode[] {
|
||||
const mems = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
const nameSet = new Set(mems.map(m => m.name));
|
||||
const ghosts = new Set<string>();
|
||||
|
||||
const nodes: MemoryNode[] = mems.map(m => ({
|
||||
name: m.name,
|
||||
missing: false,
|
||||
links: m.links,
|
||||
backlinks: m.backlinks,
|
||||
}));
|
||||
|
||||
for (const node of nodes) {
|
||||
for (const link of node.links) {
|
||||
if (!nameSet.has(link)) ghosts.add(link);
|
||||
}
|
||||
}
|
||||
|
||||
return [
|
||||
...nodes,
|
||||
...[...ghosts].map(name => ({
|
||||
name,
|
||||
missing: true,
|
||||
links: [],
|
||||
backlinks: nodes
|
||||
.filter(n => n.links.includes(name))
|
||||
.map(n => n.name),
|
||||
}))
|
||||
];
|
||||
}
|
||||
|
||||
export function renderMemoryGraph(nodes) {
|
||||
if (!nodes.length) return 'No memories yet.';
|
||||
|
||||
const groups = new Map();
|
||||
for (const node of nodes) {
|
||||
const [prefix, ...rest] = node.name.split('/');
|
||||
const group = rest.length ? prefix : 'Root';
|
||||
const label = rest.length ? rest.join('/') : node.name;
|
||||
if (!groups.has(group)) groups.set(group, []);
|
||||
groups.get(group).push({...node, label});
|
||||
}
|
||||
|
||||
const ghostCount = nodes.filter(n => n.missing).length;
|
||||
const lines = [`Memory Graph (${nodes.length} nodes, ${ghostCount} ghost${ghostCount === 1 ? '' : 's'})`, ''];
|
||||
|
||||
for (const group of [...groups.keys()].sort()) {
|
||||
const items = groups.get(group).sort((a, b) => a.label.localeCompare(b.label));
|
||||
lines.push(`${group}/`);
|
||||
items.forEach((n, i) => {
|
||||
const last = i === items.length - 1;
|
||||
const branch = last ? '└─' : '├─';
|
||||
const pad = last ? ' ' : '│ ';
|
||||
const tag = n.missing ? ' (ghost)' : '';
|
||||
lines.push(` ${branch} ${n.label}${tag}`);
|
||||
if (n.links.length) lines.push(` ${pad} → ${n.links.join(', ')}`);
|
||||
if (n.backlinks.length) lines.push(` ${pad} ← ${n.backlinks.join(', ')}`);
|
||||
});
|
||||
lines.push('');
|
||||
}
|
||||
|
||||
return lines.join('\n').trimEnd();
|
||||
}
|
||||
+5
-2
@@ -1,11 +1,14 @@
|
||||
export * from './ai';
|
||||
export * from './antrhopic';
|
||||
export * from './audio';
|
||||
export * from './helpers';
|
||||
export * from './llm';
|
||||
export * from './memory';
|
||||
export * from './memory/graph';
|
||||
export * from './memory/kd-tree';
|
||||
export * from './memory/memory';
|
||||
export * from './memory/memory-state';
|
||||
export * from './open-ai';
|
||||
export * from './provider';
|
||||
export * from './token-pool'
|
||||
export * from './tools';
|
||||
export * from './vision';
|
||||
export * from './utils';
|
||||
|
||||
+252
-78
@@ -1,19 +1,33 @@
|
||||
import {snakeCase} from '@ztimson/utils';
|
||||
import {clean, makeUnique, snakeCase} from '@ztimson/utils';
|
||||
import {AbortablePromise, Ai} from './ai.ts';
|
||||
import {Anthropic} from './antrhopic.ts';
|
||||
import {MemoryCache} from './memory/memory-state.ts';
|
||||
import {Memory, MemoryManager, MemoryOptions} from './memory/memory.ts';
|
||||
import {OpenAi} from './open-ai.ts';
|
||||
import {LLMProvider} from './provider.ts';
|
||||
import {AiTool, AiToolArg} from './tools.ts';
|
||||
import {fileURLToPath} from 'url';
|
||||
import {dirname, join} from 'path';
|
||||
import {spawn} from 'node:child_process';
|
||||
import {Memory, MemoryCache, MemoryManager, MemoryOptions} from './memory.ts';
|
||||
import {mkdtempSync} from 'node:fs';
|
||||
import fs from 'node:fs/promises';
|
||||
import {tmpdir} from 'node:os';
|
||||
import {dirname, join, basename, extname} from 'path';
|
||||
import { PDFParse } from 'pdf-parse';
|
||||
import {stripHeader} from './utils.ts';
|
||||
|
||||
const MAX_AGENT_DEPTH = 5;
|
||||
const PDF_OCR_PAGE_THRESHOLD = 12; // above this many pages, OCR scanned pages instead of feeding images to the model
|
||||
|
||||
export type AnthropicConfig = {proto: 'anthropic', token: string | string[]};
|
||||
export type OpenAiConfig = {proto: 'openai', host?: string, token: string | string[]};
|
||||
|
||||
export type AgentRef = {
|
||||
name: string;
|
||||
description?: string;
|
||||
delegate?: boolean;
|
||||
fn: () => Agent | null | Promise<Agent | null>;
|
||||
}
|
||||
|
||||
export type Agent = {
|
||||
name: string;
|
||||
description?: string;
|
||||
@@ -24,14 +38,29 @@ export type Agent = {
|
||||
skills?: Skill[] | null;
|
||||
tools?: AiTool[] | null;
|
||||
mcp?: McpServer[] | null;
|
||||
agents?: string[] | null;
|
||||
agents?: AgentRef[] | null;
|
||||
}
|
||||
|
||||
export type LLMFile = {
|
||||
/** Path to file on disk */
|
||||
path?: string;
|
||||
/** File content: raw text, base64-encoded binary, or a Buffer */
|
||||
content?: string | Buffer;
|
||||
/** Original filename, used to infer type from extension */
|
||||
name?: string;
|
||||
/** Mime type override, inferred from extension if omitted */
|
||||
mime?: string;
|
||||
/** @internal set once extraction has run, skips re-processing next turn */
|
||||
extracted?: boolean;
|
||||
};
|
||||
|
||||
export type LLMMessage = {
|
||||
/** Message originator */
|
||||
role: 'assistant' | 'system' | 'user';
|
||||
/** Message content */
|
||||
content: string | any;
|
||||
/** Files attached to request */
|
||||
files?: LLMFile[];
|
||||
/** Timestamp */
|
||||
timestamp?: number;
|
||||
/** Response duration in ms */
|
||||
@@ -67,7 +96,7 @@ export type LLMRequest = {
|
||||
/** Message history */
|
||||
history?: LLMMessage[];
|
||||
/** Max tokens for request */
|
||||
max_tokens?: number;
|
||||
maxTokens?: number;
|
||||
/** 0 = Rigid Logic, 1 = Balanced, 2 = Hyper Creative **/
|
||||
temperature?: number;
|
||||
/** Available tools */
|
||||
@@ -86,8 +115,10 @@ export type LLMRequest = {
|
||||
skills?: Skill[];
|
||||
/** MCP servers to connect and expose as tools */
|
||||
mcp?: McpServer[];
|
||||
/** Subagents exposed as delegatable/wrapped tools */
|
||||
agents?: Agent[];
|
||||
/** Subagents exposed as delegatable/wrapped tools, resolved lazily via their `fn` */
|
||||
agents?: AgentRef[];
|
||||
/** Attach files to request */
|
||||
files?: LLMFile[];
|
||||
/** @internal recursion guard for nested agent delegation */
|
||||
_agentDepth?: number;
|
||||
}
|
||||
@@ -111,6 +142,11 @@ export type Skill = {
|
||||
}
|
||||
|
||||
class LLM {
|
||||
private static AUDIO_EXT = ['wav','mp3','m4a','flac','ogg','aac','wma'];
|
||||
private static IMAGE_EXT = ['png','jpg','jpeg','bmp','gif','tiff','webp'];
|
||||
private static TEXT_EXT = ['txt','md','csv','json','xml','html','js','ts','py','yaml','yml','log'];
|
||||
private static PDF_EXT = ['pdf'];
|
||||
|
||||
private memoryManager!: MemoryManager;
|
||||
|
||||
defaultModel!: string;
|
||||
@@ -126,30 +162,140 @@ class LLM {
|
||||
this.memoryManager = new MemoryManager(this);
|
||||
}
|
||||
|
||||
private setupAgent(agents: Agent[] = [], allAgents: Agent[], history: LLMMessage[], aborts: (() => void)[], depth = 0, delegateState: {resp: string | null}): AiTool[] {
|
||||
return agents.map(a => {
|
||||
const toolName = `${a.delegate ? '' : 'sub'}agent_${snakeCase(a.name)}`;
|
||||
private async loadBuffer(file: LLMFile, asText: boolean): Promise<Buffer> {
|
||||
if(file.path) return fs.readFile(file.path);
|
||||
if(Buffer.isBuffer(file.content)) return file.content;
|
||||
if(typeof file.content === 'string') return Buffer.from(file.content, asText ? 'utf-8' : 'base64');
|
||||
throw new Error('No path or content provided');
|
||||
}
|
||||
|
||||
private async writeTemp(name: string, buffer: Buffer): Promise<string> {
|
||||
const path = join(mkdtempSync(join(tmpdir(), 'ai-file-')), name);
|
||||
await fs.writeFile(path, buffer);
|
||||
return path;
|
||||
}
|
||||
|
||||
/**
|
||||
* Extract text from a PDF. Pages with no text layer (scanned/image-only) are handled as either:
|
||||
* - Rendered to images and returned alongside the text so the (vision-capable) model can read them directly
|
||||
* - OCR'd via Tesseract when the doc is too large to reasonably pass as images
|
||||
*/
|
||||
private async resolvePdf(buffer: Buffer): Promise<{text: string, images: {mime: string, data: string}[]}> {
|
||||
const parser = new PDFParse({data: buffer});
|
||||
try {
|
||||
const {text, pages} = await parser.getText();
|
||||
const scanned = (pages || []).filter(p => !p.text?.trim());
|
||||
if(!scanned.length) return {text: text.trim() || '[Empty PDF]', images: []};
|
||||
const total = pages.length;
|
||||
const pageNums = scanned.map(p => p.num);
|
||||
const {pages: shots} = await parser.getScreenshot({partial: pageNums});
|
||||
if(total <= PDF_OCR_PAGE_THRESHOLD) {
|
||||
return {
|
||||
text: text.trim(),
|
||||
images: shots.map(s => ({mime: 'image/png', data: Buffer.from(s.data).toString('base64')}))
|
||||
};
|
||||
}
|
||||
const ocrText = await Promise.all(shots.map(async (s, i) => {
|
||||
const path = await this.writeTemp(`page-${pageNums[i]}.png`, Buffer.from(s.data));
|
||||
try {
|
||||
return await this.ai.vision.ocr(path) || '';
|
||||
} finally {
|
||||
fs.rm(dirname(path), {recursive: true, force: true}).catch(() => {});
|
||||
}
|
||||
}));
|
||||
return {text: [text.trim(), ...ocrText].filter(Boolean).join('\n\n'), images: []};
|
||||
} finally {
|
||||
await parser.destroy();
|
||||
}
|
||||
}
|
||||
|
||||
private async resolveFile(file: LLMFile): Promise<{text?: string, images?: {mime: string, data: string}[]}> {
|
||||
const name = file.name || (file.path ? basename(file.path) : 'file');
|
||||
|
||||
// Already resolved on a previous turn, reuse cached text
|
||||
if(file.extracted) return {text: `<file name="${name}">\n${file.content}\n</file>`};
|
||||
|
||||
const ext = extname(name).slice(1).toLowerCase();
|
||||
const mime = file.mime || '';
|
||||
const isAudio = mime.startsWith('audio/') || LLM.AUDIO_EXT.includes(ext);
|
||||
const isImage = mime.startsWith('image/') || LLM.IMAGE_EXT.includes(ext);
|
||||
const isPdf = mime === 'application/pdf' || LLM.PDF_EXT.includes(ext);
|
||||
const isText = mime.startsWith('text/') || LLM.TEXT_EXT.includes(ext);
|
||||
|
||||
let tmpDir: string | null = null;
|
||||
try {
|
||||
if(isImage) {
|
||||
const data = (await this.loadBuffer(file, false)).toString('base64');
|
||||
return {images: [{mime: mime || `image/${ext === 'jpg' ? 'jpeg' : ext}`, data}]};
|
||||
}
|
||||
|
||||
if(isPdf) {
|
||||
const {text, images} = await this.resolvePdf(await this.loadBuffer(file, false));
|
||||
// Only cache/skip re-processing when we didn't need to hand off images (OCR'd or fully text-based)
|
||||
if(!images.length) {
|
||||
file.content = text;
|
||||
file.extracted = true;
|
||||
delete file.path;
|
||||
}
|
||||
return {text: `<file name="${name}">\n${text || '[Scanned PDF - see attached page images]'}\n</file>`, images};
|
||||
}
|
||||
|
||||
let text: string;
|
||||
if(isAudio) {
|
||||
let path = file.path;
|
||||
if(!path) {
|
||||
const buffer = await this.loadBuffer(file, false);
|
||||
path = await this.writeTemp(name, buffer);
|
||||
tmpDir = dirname(path);
|
||||
}
|
||||
text = await this.ai.audio.asr(path) || '';
|
||||
} else if(isText) {
|
||||
text = (await this.loadBuffer(file, true)).toString('utf-8');
|
||||
} else {
|
||||
text = typeof file.content === 'string' ? file.content : `[Binary file, unable to extract: ${name}]`;
|
||||
}
|
||||
file.content = text;
|
||||
file.extracted = true;
|
||||
delete file.path;
|
||||
|
||||
return {text: `<file name="${name}">\n${text}\n</file>`};
|
||||
} catch(err: any) {
|
||||
return {text: `<file name="${name}">Failed to process: ${err.message}</file>`};
|
||||
} finally {
|
||||
if(tmpDir) fs.rm(tmpDir, {recursive: true, force: true}).catch(() => {});
|
||||
}
|
||||
}
|
||||
|
||||
private async resolveFiles(files: LLMFile[]): Promise<{text: string, images: {mime: string, data: string}[]}> {
|
||||
const resolved = await Promise.all(files.map(f => this.resolveFile(f)));
|
||||
return {
|
||||
text: resolved.filter(r => r.text).map(r => r.text).join('\n\n'),
|
||||
images: resolved.flatMap(r => r.images || [])
|
||||
};
|
||||
}
|
||||
|
||||
private setupAgent(stubs: AgentRef[] = [], history: LLMMessage[], aborts: ((keep?: boolean) => void)[], depth = 0, delegateState: {resp: string | null}): AiTool[] {
|
||||
return stubs.map(stub => {
|
||||
const toolName = `${stub.delegate ? '' : 'sub'}agent_${snakeCase(stub.name)}`;
|
||||
return {
|
||||
name: toolName,
|
||||
description: `${a.delegate ? 'Delegate to ' : ''}Subagent: ${a.description || a.name}`,
|
||||
args: <any>(a.delegate ? {} : {
|
||||
context: {type: 'string', description: 'Summary of related messages, samples, files, etc...', required: true},
|
||||
description: `${stub.delegate ? 'Delegate to ' : ''}Subagent: ${stub.description || stub.name}`,
|
||||
args: clean<any>({
|
||||
context: !stub.delegate ? {type: 'string', description: 'Summary of related messages, samples, files, etc...', required: true} : undefined,
|
||||
instructions: {type: 'string', description: 'Detailed instructions for subagent to complete', required: true},
|
||||
}),
|
||||
fn: async (args: any, stream: any, ai: any, id?: string) => {
|
||||
if(depth >= MAX_AGENT_DEPTH) return 'Max agent delegation depth exceeded';
|
||||
|
||||
const nested = (a.agents || [])
|
||||
.map(name => allAgents.find(x => x.name === name))
|
||||
.filter((x): x is Agent => !!x && x.name !== a.name);
|
||||
const a = await stub.fn();
|
||||
if(!a) return `Agent "${stub.name}" could not be resolved`;
|
||||
|
||||
// Delegate continues the SAME live conversation - no new user turn needed,
|
||||
// `history` is always current (shared, mutated in place) by the time this runs
|
||||
const q = a.delegate ? '' : `${args.instructions}${args.context ? `\n\n<context>${args.context}</context>` : ''}`;
|
||||
|
||||
const request = this.ask(q, {
|
||||
system: `You are a specialized subagent. ${a.delegate ? 'Your output streams directly to the user for the remainder of this turn. You are mid conversation - dispense with greetings.' : 'You are wrapped in a tool call that will be analysis by an LLM - dispense with conversation'}
|
||||
As a subagent, focus on executing your task completely using available tools and returning only the final result - no commentary, questions, or dialogue.
|
||||
system: `You are a specialized subagent being called from an orchestrator
|
||||
${a.delegate ? 'Your output streams directly to the user for the remainder of this turn. You are mid conversation' : 'You are wrapped in a tool call that will be analysis by an LLM'}
|
||||
Dispense with greetings and focus on your instructions using available tools and returning only the final result unless specifically instructed to converse
|
||||
|
||||
${a.system}`,
|
||||
model: a.model || undefined,
|
||||
@@ -159,7 +305,7 @@ ${a.system}`,
|
||||
mcp: a.mcp || undefined,
|
||||
skills: a.skills || undefined,
|
||||
tools: a.tools || undefined,
|
||||
agents: nested,
|
||||
agents: a.agents || [],
|
||||
_agentDepth: depth + 1,
|
||||
} as any);
|
||||
aborts.push(request.abort);
|
||||
@@ -208,7 +354,7 @@ ${a.system}`,
|
||||
|
||||
const list = allTools.map(t => `- ${t.name}: ${t.description}`).join('\n');
|
||||
return {
|
||||
prompt: `You have access to the following MCP tools:\n${list}`,
|
||||
prompt: `## MCP\nYou have access to the following MCP tools:\n${list}`,
|
||||
tools: allTools
|
||||
};
|
||||
}
|
||||
@@ -217,7 +363,7 @@ ${a.system}`,
|
||||
if(!skills?.length) return {prompt: '', tools: []};
|
||||
const list = skills.map(s => `- ${s.name}: ${s.description}`).join('\n');
|
||||
return {
|
||||
prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
|
||||
prompt: `## Skills\nYou have access to the following skill documents, whenever there is overlap between a question and a skill file, use \`skill_read\` to get instructions and background knowledge:\n${list}`,
|
||||
tools: [{
|
||||
name: 'skill_read',
|
||||
description: 'Read the full content of a skill/knowledge document',
|
||||
@@ -259,11 +405,13 @@ ${a.system}`,
|
||||
if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
|
||||
let request: AbortablePromise<string> | null = null;
|
||||
let aborted = false;
|
||||
const nestedAborts: (() => void)[] = [];
|
||||
const abort = () => {
|
||||
let keepOnAbort = true;
|
||||
const nestedAborts: ((keep?: boolean) => void)[] = [];
|
||||
const abort = (keep = true) => {
|
||||
aborted = true;
|
||||
request?.abort?.();
|
||||
nestedAborts.forEach(a => a());
|
||||
keepOnAbort = keep;
|
||||
request?.abort?.(keep);
|
||||
nestedAborts.forEach(a => a(keep));
|
||||
};
|
||||
|
||||
let promise: any;
|
||||
@@ -272,10 +420,25 @@ ${a.system}`,
|
||||
promise = (async () => {
|
||||
let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
|
||||
const prompts: string[] = [];
|
||||
// `history` is the single source of truth from here on - mutated in place by
|
||||
// this call AND by any nested/delegated agent calls sharing the same array
|
||||
let history = options.history || [];
|
||||
if(message) history.push({role: 'user', content: message, timestamp: Date.now()});
|
||||
const historyStart = history.length;
|
||||
const files = options.files || [];
|
||||
if(message || files.length) history.push({role: 'user', content: message || '', timestamp: Date.now()});
|
||||
|
||||
// Accumulate streamed text so it can be committed to history if aborted mid-generation
|
||||
let partialText = '';
|
||||
const onStream = options.stream;
|
||||
const stream = (chunk: {text?: string, tool?: string, done?: true}) => {
|
||||
if(chunk.text) partialText += chunk.text;
|
||||
return onStream?.(chunk);
|
||||
};
|
||||
|
||||
/** Commit (keep) or discard this turn's progress on abort, then throw */
|
||||
const abortNow = (): never => {
|
||||
if(keepOnAbort) { if(partialText) history.push({role: 'assistant', content: partialText, timestamp: Date.now()}); }
|
||||
else history.splice(historyStart, history.length - historyStart);
|
||||
throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
|
||||
};
|
||||
|
||||
// MCP
|
||||
const mcp = options.mcp || this.ai.options?.llm?.mcp;
|
||||
@@ -296,7 +459,7 @@ ${a.system}`,
|
||||
// Agents
|
||||
const agents = options.agents || this.ai.options?.llm?.agents;
|
||||
const delegateState: {resp: string | null} = {resp: null};
|
||||
if(agents?.length) tools.push(...this.setupAgent(agents, agents, history, nestedAborts, options._agentDepth || 0, delegateState));
|
||||
if(agents?.length) tools.push(...this.setupAgent(agents, history, nestedAborts, options._agentDepth || 0, delegateState));
|
||||
|
||||
// Memory
|
||||
const mem = MemoryManager.normalize(options.memory);
|
||||
@@ -304,8 +467,8 @@ ${a.system}`,
|
||||
const mems = mem.memory instanceof MemoryCache ? mem.memory.memories : mem.memory;
|
||||
if(mems.length) {
|
||||
if(mem.inject) {
|
||||
const pool = 15; // candidates considered, cheap since only refs are listed
|
||||
const budget = mem.maxTokens ?? 2000; // actual content injected
|
||||
const pool = 15;
|
||||
const budget = mem.maxTokens ?? 2000;
|
||||
const relevant = await this.memoryManager.recollect(message, mem.memory, pool);
|
||||
|
||||
let used = 0;
|
||||
@@ -319,35 +482,64 @@ ${a.system}`,
|
||||
} else listed.push(r);
|
||||
}
|
||||
|
||||
prompts.unshift(`You have access to the following memory files:
|
||||
${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}
|
||||
${preloaded.length ? `
|
||||
Relevant memories have been preloaded:
|
||||
${preloaded.map(r => `
|
||||
**${r.name}**
|
||||
${r.description}
|
||||
${r.content}
|
||||
`).join('\n---\n')}
|
||||
` : ''}${listed.length ? `
|
||||
Also relevant but not preloaded (use \`memory_recall\`): ${listed.map(r => r.name).join(', ')}
|
||||
` : ''}`.trim());
|
||||
prompts.unshift(`## Memory
|
||||
You have a background memory process which has prefetched relevant information${mem.update ? ' and will create new memories from this conversation' : ''} for you
|
||||
Assume it is perfect and never mention this process to anyone ever
|
||||
Always use your memories to craft a personalized response, they contain links / [[wiki links]] which you use navigate between them
|
||||
${mem.tool ? `You can access memory files via the \`memory_search\` and \`memory_recall\` tools
|
||||
When you need information about the user, \`memory_recall\` \`People/User\` before asking (fetch if not included bellow)
|
||||
When you need information not provided, attempt 1-3 \`memory_search\` calls with distinct queries before asking` : ''}
|
||||
|
||||
${preloaded.length ? `### Prefetched Memories (Most relevant first):
|
||||
|
||||
${preloaded.map(r => `Memory: ${r.name}
|
||||
Description: ${r.description}
|
||||
Linked: ${makeUnique([...r.links, ...r.backlinks]).join(', ')}
|
||||
\`\`\`
|
||||
${stripHeader(r.content)}
|
||||
\`\`\``).join('\n\n')}` : ''}
|
||||
${mem.tool && listed.length ? '\n' + listed.map(r => `Memory: ${r.name}
|
||||
Description: ${r.description}
|
||||
Linked: ${makeUnique([...r.links, ...r.backlinks]).join(', ')}
|
||||
<!-- Truncated -->`).join('\n\n') : ''}`.trim())
|
||||
}
|
||||
if(mem.tool) tools.push(this.memoryManager.tools.read(mem.memory));
|
||||
if(mem.tool) tools.push(...this.memoryManager.tools.read(mem.memory));
|
||||
}
|
||||
}
|
||||
|
||||
if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
|
||||
if(aborted) abortNow();
|
||||
|
||||
const lastMsg = history[history.length - 1];
|
||||
if(files.length && lastMsg?.role === 'user') lastMsg.files = files;
|
||||
const restores: {msg: LLMMessage, content: any}[] = [];
|
||||
for(const msg of history) {
|
||||
if(msg.role !== 'user' || !msg.files?.length) continue;
|
||||
const {text, images} = await this.resolveFiles(msg.files);
|
||||
if(!text && !images.length) continue;
|
||||
restores.push({msg, content: msg.content});
|
||||
const merged = text ? [msg.content, text].filter(Boolean).join('\n\n') : msg.content;
|
||||
msg.content = images.length
|
||||
? [...images.map(i => ({type: 'image', mime: i.mime, data: i.data})), {type: 'text', text: merged}]
|
||||
: merged;
|
||||
}
|
||||
|
||||
const toolTimings = new Map<string, {duration: number, tps: number}>();
|
||||
tools = this.wrapToolTiming(tools, toolTimings);
|
||||
|
||||
if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
|
||||
if(aborted) abortNow();
|
||||
|
||||
prompts.unshift(options.system || this.ai.options.llm?.system || '');
|
||||
// Message already appended to shared `history` above - pass '' so the provider
|
||||
// doesn't push a duplicate user turn
|
||||
request = this.models[m].ask('', {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
|
||||
let resp = await request;
|
||||
request = this.models[m].ask('', {...options, tools, stream, system: prompts.filter(Boolean).join('\n\n')});
|
||||
let resp: string;
|
||||
try {
|
||||
resp = await request;
|
||||
} catch(err: any) {
|
||||
if(aborted) return abortNow();
|
||||
throw err;
|
||||
}
|
||||
|
||||
// Strip the file injection shim
|
||||
restores.forEach(({msg, content}) => msg.content = content);
|
||||
|
||||
// Capture meta (duration / tps)
|
||||
for(const h of history) {
|
||||
@@ -376,14 +568,6 @@ Also relevant but not preloaded (use \`memory_recall\`): ${listed.map(r => r.nam
|
||||
return Object.assign(promise, {abort});
|
||||
}
|
||||
|
||||
/**
|
||||
* Digest full conversation history into memory documents.
|
||||
* Call on session end to persist the conversation.
|
||||
*/
|
||||
async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<Memory[]> {
|
||||
return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
|
||||
}
|
||||
|
||||
/**
|
||||
* Compress chat history to reduce context size
|
||||
* @param {LLMMessage[]} history Chatlog that will be compressed
|
||||
@@ -412,24 +596,6 @@ Also relevant but not preloaded (use \`memory_recall\`): ${listed.map(r => r.nam
|
||||
return h;
|
||||
}
|
||||
|
||||
/**
|
||||
* Compare the difference between embeddings (calculates the angle between two vectors)
|
||||
* @param {number[]} v1 First embedding / vector comparison
|
||||
* @param {number[]} v2 Second embedding / vector for comparison
|
||||
* @returns {number} Similarity values 0-1: 0 = unique, 1 = identical
|
||||
*/
|
||||
cosineSimilarity(v1: number[], v2: number[]): number {
|
||||
if (v1.length !== v2.length) throw new Error('Vectors must be same length');
|
||||
let dotProduct = 0, normA = 0, normB = 0;
|
||||
for (let i = 0; i < v1.length; i++) {
|
||||
dotProduct += v1[i] * v2[i];
|
||||
normA += v1[i] * v1[i];
|
||||
normB += v2[i] * v2[i];
|
||||
}
|
||||
const denominator = Math.sqrt(normA) * Math.sqrt(normB);
|
||||
return denominator === 0 ? 0 : dotProduct / denominator;
|
||||
}
|
||||
|
||||
/**
|
||||
* Chunk text into parts for AI digestion
|
||||
* @param {object | string} target Item that will be chunked (objects get converted)
|
||||
@@ -563,6 +729,14 @@ Also relevant but not preloaded (use \`memory_recall\`): ${listed.map(r => r.nam
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Digest full conversation history into memory documents.
|
||||
* Call on session end to persist the conversation.
|
||||
*/
|
||||
async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<Memory[]> {
|
||||
return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a summary of some text
|
||||
* @param {string} text Text to summarize
|
||||
|
||||
-502
@@ -1,502 +0,0 @@
|
||||
import {LLMRequest, LLMMessage} from './llm.ts';
|
||||
import {AiTool} from './tools.ts';
|
||||
import {KDPoint, KDTree} from './kd-tree.ts';
|
||||
|
||||
const FACTS_HEADING = '## Facts';
|
||||
|
||||
const GENERIC_TEMPLATE = `# {{Title}}
|
||||
|
||||
## Summary
|
||||
|
||||
## Details
|
||||
|
||||
## Related`;
|
||||
|
||||
export class MemoryCache {
|
||||
private tree: KDTree<MemoryRef>;
|
||||
public memories: Memory[];
|
||||
|
||||
get length() { return this.memories.length; }
|
||||
|
||||
constructor(memories: Memory[]) {
|
||||
this.memories = memories;
|
||||
this.tree = this.buildTree();
|
||||
}
|
||||
|
||||
private buildTree(): KDTree<MemoryRef> {
|
||||
const embedded = this.memories.filter(m => m.embedding?.length);
|
||||
if (!embedded.length) return new KDTree<MemoryRef>(0);
|
||||
|
||||
const dims = embedded[0].embedding.length;
|
||||
const points: KDPoint<MemoryRef>[] = embedded.map(m => ({
|
||||
vector: m.embedding,
|
||||
payload: {name: m.name, description: m.description},
|
||||
}));
|
||||
|
||||
return new KDTree<MemoryRef>(dims, 'cosine', points);
|
||||
}
|
||||
|
||||
search(query: number[], limit: number): MemoryRef[] {
|
||||
const results = this.tree.knn(query, limit);
|
||||
return results.map(r => r.point.payload);
|
||||
}
|
||||
|
||||
add(memory: Memory): void {
|
||||
this.memories.push(memory);
|
||||
this.rebuild();
|
||||
}
|
||||
|
||||
update(memory: Memory): void {
|
||||
const idx = this.memories.findIndex(m => m.name === memory.name);
|
||||
if (idx !== -1) {
|
||||
this.memories[idx] = memory;
|
||||
} else {
|
||||
this.memories.push(memory);
|
||||
}
|
||||
this.rebuild();
|
||||
}
|
||||
|
||||
remove(name: string): void {
|
||||
const idx = this.memories.findIndex(m => m.name === name);
|
||||
if (idx !== -1) {
|
||||
this.memories.splice(idx, 1);
|
||||
this.rebuild();
|
||||
}
|
||||
}
|
||||
|
||||
rebuild(): void {
|
||||
this.tree = this.buildTree();
|
||||
}
|
||||
}
|
||||
|
||||
export type MemoryOptions = {
|
||||
/** Memory object */
|
||||
memory: Memory[] | MemoryCache;
|
||||
/** Inject N memories into the system prompt */
|
||||
inject?: boolean;
|
||||
/** expose recall tool to LLM */
|
||||
tool?: boolean;
|
||||
/** Update memory on compression */
|
||||
update?: boolean;
|
||||
/** Max context size of memories to inject to each call (removed immediately after use) */
|
||||
maxTokens?: number;
|
||||
}
|
||||
|
||||
export type Memory = {
|
||||
name: string;
|
||||
description: string;
|
||||
content: string;
|
||||
embedding: number[];
|
||||
links: string[];
|
||||
backlinks: string[];
|
||||
}
|
||||
|
||||
type MemoryRef = {
|
||||
name: string;
|
||||
description: string;
|
||||
}
|
||||
|
||||
type FactBucket = {
|
||||
subject: string;
|
||||
facts: 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 rebuildGraph(memories: Memory[]): void {
|
||||
for (const m of memories) m.links = extractLinks(m.content).filter(l => l !== m.name);
|
||||
for (const m of memories) m.backlinks = [];
|
||||
for (const m of memories) {
|
||||
for (const link of m.links) {
|
||||
const target = memories.find(t => t.name === link);
|
||||
if (target) target.backlinks.push(m.name);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
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);
|
||||
}
|
||||
|
||||
export class MemoryManager {
|
||||
private recentlyTouched = new Map<string, number>();
|
||||
|
||||
private queues = new Map<string, {
|
||||
dirty: boolean,
|
||||
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 = this.unwrap(memories);
|
||||
const mem = mems.find(m => m.name === args.name);
|
||||
if (!mem) return 'Document not found';
|
||||
this.touch(mem.name);
|
||||
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: ${args.name}`;
|
||||
},
|
||||
}),
|
||||
};
|
||||
|
||||
constructor(private llm: any) {}
|
||||
|
||||
static normalize(m?: Memory[] | MemoryCache | MemoryOptions) {
|
||||
if(!m) return null;
|
||||
const raw = m instanceof MemoryCache || Array.isArray(m);
|
||||
return raw ? {memory: <Memory[] | MemoryCache>m, inject: true, tool: true, update: true} : {inject: true, tool: true, update: true, ...m};
|
||||
}
|
||||
|
||||
private unwrap(memories: Memory[] | MemoryCache): Memory[] {
|
||||
return memories instanceof MemoryCache ? memories.memories : memories;
|
||||
}
|
||||
|
||||
private sync(memories: Memory[] | MemoryCache): void {
|
||||
if (memories instanceof MemoryCache) memories.rebuild();
|
||||
}
|
||||
|
||||
private parseFrontmatter(content: string): {fm: Map<string, string>, body: string} {
|
||||
const match = content.match(/^---\n([\s\S]*?)\n---\n?([\s\S]*)$/);
|
||||
if (!match) return {fm: new Map(), body: content};
|
||||
const fm = new Map<string, string>();
|
||||
for (const line of match[1].split('\n')) {
|
||||
const i = line.indexOf(':');
|
||||
if (i === -1) continue;
|
||||
fm.set(line.slice(0, i).trim(), line.slice(i + 1).trim());
|
||||
}
|
||||
return {fm, body: match[2]};
|
||||
}
|
||||
|
||||
private writeFrontmatter(fm: Map<string, string>, body: string): string {
|
||||
const lines = [...fm.entries()].map(([k, v]) => `${k}: ${v}`);
|
||||
return `---\n${lines.join('\n')}\n---\n\n${body.trimStart()}`;
|
||||
}
|
||||
|
||||
private stripHeader(content: string): string {
|
||||
return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
|
||||
}
|
||||
|
||||
private touchHeader(node: Memory, body: string): string {
|
||||
const {fm} = this.parseFrontmatter(node.content);
|
||||
fm.set('name', node.name);
|
||||
fm.set('description', node.description || '');
|
||||
fm.set('modified', new Date().toISOString());
|
||||
return this.writeFrontmatter(fm, body);
|
||||
}
|
||||
|
||||
private ensureDoc(node: Memory): void {
|
||||
if (node.content) return;
|
||||
const title = node.name.split('/').pop() ?? node.name;
|
||||
node.content = this.touchHeader(node, `# ${title}\n`);
|
||||
}
|
||||
|
||||
private appendFacts(node: Memory, facts: string[]): void {
|
||||
this.ensureDoc(node);
|
||||
const body = this.stripHeader(node.content);
|
||||
const bullets = facts.map(f => `- ${f}`).join('\n');
|
||||
const idx = body.indexOf(FACTS_HEADING);
|
||||
const newBody = idx === -1
|
||||
? `${body.trimEnd()}\n\n${FACTS_HEADING}\n${bullets}\n`
|
||||
: `${body.slice(0, idx + FACTS_HEADING.length)}\n${bullets}${body.slice(idx + FACTS_HEADING.length)}`;
|
||||
node.content = this.touchHeader(node, newBody);
|
||||
}
|
||||
|
||||
decay() {
|
||||
for(const [name, ttl] of this.recentlyTouched) {
|
||||
if(ttl <= 1) this.recentlyTouched.delete(name);
|
||||
else this.recentlyTouched.set(name, ttl - 1);
|
||||
}
|
||||
}
|
||||
|
||||
touch(name: string, ttl = 2) {
|
||||
this.recentlyTouched.set(name, ttl);
|
||||
}
|
||||
|
||||
getTouched(): string[] {
|
||||
return [...this.recentlyTouched.keys()];
|
||||
}
|
||||
|
||||
forget(name: string, memories: Memory[] | MemoryCache): boolean {
|
||||
const mem = this.unwrap(memories);
|
||||
const idx = mem.findIndex(m => m.name === name);
|
||||
if (idx === -1) return false;
|
||||
|
||||
mem.splice(idx, 1);
|
||||
rebuildGraph(mem);
|
||||
this.sync(memories);
|
||||
return true;
|
||||
}
|
||||
|
||||
async recollect(query: string, memories: Memory[] | MemoryCache, limit = 5, graphDepth = 1): Promise<Memory[]> {
|
||||
const mem = this.unwrap(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;
|
||||
for (const link of node.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);
|
||||
}
|
||||
|
||||
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 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}]: ${h.content}`).join('\n\n').trim();
|
||||
if (!conversation) return [];
|
||||
|
||||
const uid = `${Date.now()}_${Math.random().toString(36).slice(2)}`;
|
||||
// NOTE: adjust field names below (id/tool_call_id/name) to match your LLMMessage/tool-call schema.
|
||||
const pending = {role: 'tool', name: 'memory_process', id: uid, content: 'Processing…'} as unknown as LLMMessage;
|
||||
history.push(pending);
|
||||
|
||||
const mem = this.unwrap(memories);
|
||||
const buckets = await this.factAgent(conversation, mem, options, getWeekMonday());
|
||||
const touched: Memory[] = [];
|
||||
|
||||
for (const {subject, facts} of buckets) {
|
||||
let node = mem.find(m => m.name === subject);
|
||||
if (!node) {
|
||||
node = {name: subject, description: '', content: '', embedding: [], links: [], backlinks: []};
|
||||
mem.push(node);
|
||||
}
|
||||
this.appendFacts(node, facts);
|
||||
const [e] = await this.llm.embedding(node.content);
|
||||
if (e) node.embedding = e.embedding;
|
||||
this.touch(node.name);
|
||||
touched.push(node);
|
||||
}
|
||||
|
||||
if (touched.length) {
|
||||
rebuildGraph(mem);
|
||||
this.sync(memories);
|
||||
(pending as any).content = `Saved to ${touched.map(n => `[[${n.name}]]`).join(', ')}`;
|
||||
for (const node of touched) this.reconcile(node, memories, options).catch(() => {});
|
||||
} else {
|
||||
(pending as any).content = 'Nothing worth remembering.';
|
||||
}
|
||||
|
||||
return touched;
|
||||
}
|
||||
|
||||
/** Manual/cron entry point. scope 'touched' only reconciles docs with a pending Facts inbox. */
|
||||
async reconcileVault(memories: Memory[] | MemoryCache, options: LLMRequest, scope: 'touched' | 'all' = 'touched'): Promise<void> {
|
||||
const mem = this.unwrap(memories);
|
||||
const targets = scope === 'all' ? mem : mem.filter(m => m.content.includes(FACTS_HEADING));
|
||||
await Promise.all(targets.map(node => this.reconcile(node, memories, options)));
|
||||
this.sync(memories);
|
||||
}
|
||||
|
||||
/**
|
||||
* Coalescing queue: if a doc is already reconciling, mark it dirty and abort the in-flight
|
||||
* request. The loop below always re-reads node.content fresh, so nothing is ever dropped.
|
||||
*/
|
||||
private reconcile(node: Memory, memories: Memory[] | MemoryCache, options: LLMRequest): Promise<void> {
|
||||
const key = node.name;
|
||||
const existing = this.queues.get(key);
|
||||
if (existing) {
|
||||
existing.dirty = true;
|
||||
existing.request?.abort?.();
|
||||
return existing.task;
|
||||
}
|
||||
|
||||
const entry = {dirty: false, request: null, task: Promise.resolve()};
|
||||
this.queues.set(key, entry);
|
||||
const mem = this.unwrap(memories);
|
||||
entry.task = (async () => {
|
||||
do {
|
||||
entry.dirty = false;
|
||||
await this.reconcileDoc(node, mem, options, entry);
|
||||
} while (entry.dirty);
|
||||
})().finally(() => {
|
||||
this.queues.delete(key);
|
||||
rebuildGraph(mem);
|
||||
this.sync(memories);
|
||||
});
|
||||
return entry.task;
|
||||
}
|
||||
|
||||
private async reconcileDoc(node: Memory, memories: Memory[], options: LLMRequest, entry: {request: {abort?: () => void} | null}): Promise<void> {
|
||||
const currentBody = this.stripHeader(node.content);
|
||||
let update;
|
||||
try {
|
||||
for (let i = 0; i < 2 && !update?.content; i++) {
|
||||
const request = this.llm.ask(currentBody, {
|
||||
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 body in markdown, without the frontmatter block', required: true},
|
||||
},
|
||||
system: `You are a knowledge base editor maintaining one document in an Obsidian-style vault.
|
||||
|
||||
If the document has a "${FACTS_HEADING}" section, integrate every bullet under it into the appropriate part of the document, then remove the "${FACTS_HEADING}" section entirely. If there is no such section, just tidy the document per the rules below.
|
||||
|
||||
Structure: follow this generic shape loosely, adapting section names/order to what the content actually needs (e.g. journal-style docs may want a timeline instead of "Details"):
|
||||
\`\`\`markdown
|
||||
${GENERIC_TEMPLATE}
|
||||
\`\`\`
|
||||
|
||||
Formatting rules:
|
||||
- Use Obsidian-style markdown: # headings, **bold** for emphasis, bullet & numbered lists for grouped 1D data, 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, but skip generics (car, red, dog)
|
||||
- Keep the document concise, factual, and human-readable
|
||||
- Resolve contradictions: newer facts always win — delete the outdated statement entirely, never keep both
|
||||
- Do not add frontmatter blocks, filler, preamble, or AI commentary
|
||||
|
||||
Other nodes in the vault (link to these instead of duplicating their content):
|
||||
${this.listNodes(memories).filter(n => n.name !== node.name).map(n => n.name).join(', ') || 'none'}
|
||||
|
||||
Current document:
|
||||
\`\`\`markdown
|
||||
${currentBody}
|
||||
\`\`\``,
|
||||
});
|
||||
entry.request = request;
|
||||
update = await request;
|
||||
}
|
||||
} catch (err: any) {
|
||||
if (err?.name === 'AbortError') return;
|
||||
throw err;
|
||||
} finally {
|
||||
entry.request = null;
|
||||
}
|
||||
|
||||
if (!update?.content) return;
|
||||
node.description = node.name !== 'People/User' ? update.description : 'All information about the current user';
|
||||
node.content = this.touchHeader(node, update.content);
|
||||
const [e] = await this.llm.embedding(node.content);
|
||||
if (e) node.embedding = e.embedding;
|
||||
}
|
||||
|
||||
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 primarily about the user should go under "People/User"
|
||||
- When required, create a new path following collection/subject format (e.g., People/Sarah, Projects/Oxide) — you are not limited to any fixed list of collections, use whatever fits
|
||||
- For journal entries, use "Journal"
|
||||
|
||||
Available nodes:
|
||||
- Journal
|
||||
${this.listNodes(memories).filter(n => !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}));
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,128 @@
|
||||
import {MemoryCache} from './memory-state.ts';
|
||||
import type {Memory} from './memory.ts';
|
||||
|
||||
export type MemoryNode = {
|
||||
name: string;
|
||||
missing: boolean;
|
||||
links: string[];
|
||||
backlinks: string[];
|
||||
}
|
||||
|
||||
export function extractLinks(content: string): string[] {
|
||||
if (!content) return [];
|
||||
const matches = content.matchAll(/\[\[([^\]|]+)(?:\|[^\]]*)?\]\]/g);
|
||||
return [...new Set([...matches].map(m => m[1].trim()))];
|
||||
}
|
||||
|
||||
export function patchGraph(mems: Memory[], nodes: MemoryNode[], changed: Memory[]): MemoryNode[] {
|
||||
const nameSet = new Set(mems.map(m => m.name));
|
||||
const byName = new Map(nodes.map(n => [n.name, n]));
|
||||
|
||||
const ensureNode = (name: string): MemoryNode => {
|
||||
let n = byName.get(name);
|
||||
if (!n) {
|
||||
n = {name, missing: !nameSet.has(name), links: [], backlinks: []};
|
||||
byName.set(name, n);
|
||||
}
|
||||
return n;
|
||||
};
|
||||
|
||||
for (const m of changed) {
|
||||
const node = ensureNode(m.name);
|
||||
node.missing = false; // real memory, promotes any pre-existing ghost entry
|
||||
const oldLinks = m.links ?? [];
|
||||
const newLinks = extractLinks(m.content).filter(l => l !== m.name);
|
||||
|
||||
for (const target of oldLinks.filter(l => !newLinks.includes(l))) {
|
||||
const t = byName.get(target);
|
||||
if (!t) continue;
|
||||
t.backlinks = t.backlinks.filter(n => n !== m.name);
|
||||
if (t.missing && !t.backlinks.length) byName.delete(target); // fully dereferenced ghost
|
||||
}
|
||||
for (const target of newLinks.filter(l => !oldLinks.includes(l))) {
|
||||
const t = ensureNode(target);
|
||||
if (!t.backlinks.includes(m.name)) t.backlinks.push(m.name);
|
||||
}
|
||||
|
||||
m.links = newLinks;
|
||||
node.links = newLinks;
|
||||
}
|
||||
|
||||
for (const m of mems) {
|
||||
const n = byName.get(m.name);
|
||||
if (n) m.backlinks = n.backlinks;
|
||||
}
|
||||
|
||||
return [...byName.values()];
|
||||
}
|
||||
|
||||
export function rebuildGraph(memories: Memory[] | MemoryCache): MemoryNode[] {
|
||||
const mems = memories instanceof MemoryCache ? memories.memories : memories;
|
||||
const nameSet = new Set(mems.map(m => m.name));
|
||||
|
||||
for (const m of mems) m.links = extractLinks(m.content).filter(l => l !== m.name);
|
||||
for (const m of mems) m.backlinks = [];
|
||||
for (const m of mems) {
|
||||
for (const link of m.links) {
|
||||
const target = mems.find(t => t.name === link);
|
||||
if (target) target.backlinks.push(m.name);
|
||||
}
|
||||
}
|
||||
|
||||
const nodes: MemoryNode[] = mems.map(m => ({
|
||||
name: m.name,
|
||||
missing: false,
|
||||
links: m.links,
|
||||
backlinks: m.backlinks,
|
||||
}));
|
||||
|
||||
const ghosts = new Set<string>();
|
||||
for (const node of nodes) {
|
||||
for (const link of node.links) {
|
||||
if (!nameSet.has(link)) ghosts.add(link);
|
||||
}
|
||||
}
|
||||
|
||||
return [
|
||||
...nodes,
|
||||
...[...ghosts].map(name => ({
|
||||
name,
|
||||
missing: true,
|
||||
links: [],
|
||||
backlinks: nodes.filter(n => n.links.includes(name)).map(n => n.name),
|
||||
})),
|
||||
];
|
||||
}
|
||||
|
||||
export function renderMemoryGraph(nodes: MemoryNode[]): string {
|
||||
if (!nodes.length) return 'No memories yet.';
|
||||
|
||||
const groups = new Map<string, (MemoryNode & {label: string})[]>();
|
||||
for (const node of nodes) {
|
||||
const [prefix, ...rest] = node.name.split('/');
|
||||
const group = rest.length ? prefix : 'Root';
|
||||
const label = rest.length ? rest.join('/') : node.name;
|
||||
if (!groups.has(group)) groups.set(group, []);
|
||||
groups.get(group)!.push({...node, label});
|
||||
}
|
||||
|
||||
const ghostCount = nodes.filter(n => n.missing).length;
|
||||
const lines = [`Memory Graph (${nodes.length} nodes, ${ghostCount} ghost${ghostCount === 1 ? '' : 's'})`, ''];
|
||||
|
||||
for (const group of [...groups.keys()].sort()) {
|
||||
const items = groups.get(group)!.sort((a, b) => a.label.localeCompare(b.label));
|
||||
lines.push(`${group}/`);
|
||||
items.forEach((n, i) => {
|
||||
const last = i === items.length - 1;
|
||||
const branch = last ? '└─' : '├─';
|
||||
const pad = last ? ' ' : '│ ';
|
||||
const tag = n.missing ? ' (ghost)' : '';
|
||||
lines.push(` ${branch} ${n.label}${tag}`);
|
||||
if (n.links.length) lines.push(` ${pad} → ${n.links.join(', ')}`);
|
||||
if (n.backlinks.length) lines.push(` ${pad} ← ${n.backlinks.join(', ')}`);
|
||||
});
|
||||
lines.push('');
|
||||
}
|
||||
|
||||
return lines.join('\n').trimEnd();
|
||||
}
|
||||
@@ -1,3 +1,5 @@
|
||||
import {cosineDistance, euclideanDistance} from '../utils.ts';
|
||||
|
||||
export type DistanceMetric = "euclidean" | "cosine";
|
||||
|
||||
export interface KDPoint<T = unknown> {
|
||||
@@ -15,28 +17,7 @@ interface KDNode<T> {
|
||||
axis: number;
|
||||
left: KDNode<T> | null;
|
||||
right: KDNode<T> | null;
|
||||
}
|
||||
|
||||
// ─── Distance helpers ─────────────────────────────────────────────────────────
|
||||
|
||||
function euclidean(a: number[], b: number[]): number {
|
||||
let sum = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
const d = a[i] - b[i];
|
||||
sum += d * d;
|
||||
}
|
||||
return Math.sqrt(sum);
|
||||
}
|
||||
|
||||
function cosine(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; // distance = 1 - similarity
|
||||
deleted?: boolean;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -95,6 +76,7 @@ class BoundedMaxHeap<T> {
|
||||
*
|
||||
* Supports:
|
||||
* - Insertion of labeled points
|
||||
* - Lazy (tombstone) removal, physically purged on rebalance()
|
||||
* - k-nearest-neighbor (KNN) search
|
||||
* - Radius search (all points within a given distance)
|
||||
* - Euclidean and cosine distance metrics
|
||||
@@ -103,9 +85,11 @@ class BoundedMaxHeap<T> {
|
||||
export class KDTree<T = unknown> {
|
||||
private root: KDNode<T> | null = null;
|
||||
private _size = 0;
|
||||
private readonly dims: number;
|
||||
private _tombstones = 0;
|
||||
private readonly distanceFn: (a: number[], b: number[]) => number;
|
||||
|
||||
readonly dims: number;
|
||||
|
||||
/**
|
||||
* @param dims Dimensionality of all vectors (must be consistent).
|
||||
* @param metric Distance metric to use. Default: "euclidean".
|
||||
@@ -119,7 +103,7 @@ export class KDTree<T = unknown> {
|
||||
points?: KDPoint<T>[]
|
||||
) {
|
||||
this.dims = dims;
|
||||
this.distanceFn = metric === "cosine" ? cosine : euclidean;
|
||||
this.distanceFn = metric === "cosine" ? cosineDistance : euclideanDistance;
|
||||
|
||||
if (points && points.length > 0) {
|
||||
this.validateAll(points);
|
||||
@@ -128,9 +112,15 @@ export class KDTree<T = unknown> {
|
||||
}
|
||||
}
|
||||
|
||||
/** Total number of points stored in the tree. */
|
||||
/** Total number of live points stored in the tree (excludes tombstoned). */
|
||||
get size(): number { return this._size; }
|
||||
|
||||
/** Fraction of physical nodes that are tombstoned (pending removal on next rebalance). */
|
||||
get tombstoneRatio(): number {
|
||||
const total = this._size + this._tombstones;
|
||||
return total ? this._tombstones / total : 0;
|
||||
}
|
||||
|
||||
// ── Insertion ──────────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
@@ -143,10 +133,36 @@ export class KDTree<T = unknown> {
|
||||
this._size++;
|
||||
}
|
||||
|
||||
// ── Removal ────────────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Lazily remove all live points whose payload matches `predicate`.
|
||||
* O(n) traversal, but avoids a full tree rebuild. Call `rebalance()`
|
||||
* periodically (e.g. once tombstoneRatio crosses ~0.25) to reclaim space
|
||||
* and restore optimal query depth.
|
||||
* @returns number of points removed
|
||||
*/
|
||||
remove(predicate: (payload: T) => boolean): number {
|
||||
let removed = 0;
|
||||
const visit = (node: KDNode<T> | null): void => {
|
||||
if (!node) return;
|
||||
if (!node.deleted && predicate(node.point.payload)) {
|
||||
node.deleted = true;
|
||||
removed++;
|
||||
}
|
||||
visit(node.left);
|
||||
visit(node.right);
|
||||
};
|
||||
visit(this.root);
|
||||
this._size -= removed;
|
||||
this._tombstones += removed;
|
||||
return removed;
|
||||
}
|
||||
|
||||
// ── KNN search ─────────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Find the k nearest neighbors to `query`.
|
||||
* Find the k nearest live neighbors to `query`.
|
||||
* Returns results sorted by distance ascending.
|
||||
*/
|
||||
knn(query: number[], k: number): KNNResult<T>[] {
|
||||
@@ -170,7 +186,7 @@ export class KDTree<T = unknown> {
|
||||
// ── Radius search ──────────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Return all points whose distance to `query` is ≤ `radius`,
|
||||
* Return all live points whose distance to `query` is ≤ `radius`,
|
||||
* sorted by distance ascending.
|
||||
*/
|
||||
radiusSearch(query: number[], radius: number): KNNResult<T>[] {
|
||||
@@ -185,7 +201,7 @@ export class KDTree<T = unknown> {
|
||||
|
||||
// ── Conversion ─────────────────────────────────────────────────────────────
|
||||
|
||||
/** Collect all points in the tree (order not guaranteed). */
|
||||
/** Collect all live points in the tree (order not guaranteed). */
|
||||
toArray(): KDPoint<T>[] {
|
||||
const out: KDPoint<T>[] = [];
|
||||
this.collect(this.root, out);
|
||||
@@ -193,12 +209,14 @@ export class KDTree<T = unknown> {
|
||||
}
|
||||
|
||||
/**
|
||||
* Rebuild the tree from its current points as a balanced tree.
|
||||
* Useful after many individual insertions to restore O(log n) query time.
|
||||
* Rebuild the tree from its current live points as a balanced tree.
|
||||
* Physically purges tombstones and restores O(log n) query time.
|
||||
*/
|
||||
rebalance(): void {
|
||||
const points = this.toArray();
|
||||
this.root = points.length ? this.buildBalanced(points, 0) : null;
|
||||
this._size = points.length;
|
||||
this._tombstones = 0;
|
||||
}
|
||||
|
||||
// ── Private: build ─────────────────────────────────────────────────────────
|
||||
@@ -250,8 +268,10 @@ export class KDTree<T = unknown> {
|
||||
): void {
|
||||
if (node === null) return;
|
||||
|
||||
if (!node.deleted) {
|
||||
const dist = this.distanceFn(query, node.point.vector);
|
||||
heap.push({ point: node.point, distance: dist });
|
||||
}
|
||||
|
||||
const axis = node.axis;
|
||||
const diff = query[axis] - node.point.vector[axis];
|
||||
@@ -260,11 +280,8 @@ export class KDTree<T = unknown> {
|
||||
: [node.right, node.left];
|
||||
|
||||
this.searchKNN(near, query, k, heap, depth + 1);
|
||||
|
||||
// Only explore the far side if it could contain a closer point.
|
||||
// For cosine distance we can't prune by axis gap alone, so always explore.
|
||||
const shouldExplore =
|
||||
this.distanceFn === cosine
|
||||
this.distanceFn === cosineDistance
|
||||
? true
|
||||
: Math.abs(diff) < heap.worstDistance;
|
||||
|
||||
@@ -284,10 +301,12 @@ export class KDTree<T = unknown> {
|
||||
): void {
|
||||
if (node === null) return;
|
||||
|
||||
if (!node.deleted) {
|
||||
const dist = this.distanceFn(query, node.point.vector);
|
||||
if (dist <= radius) {
|
||||
results.push({ point: node.point, distance: dist });
|
||||
}
|
||||
}
|
||||
|
||||
const axis = node.axis;
|
||||
const diff = query[axis] - node.point.vector[axis];
|
||||
@@ -298,7 +317,7 @@ export class KDTree<T = unknown> {
|
||||
this.searchRadius(near, query, radius, results, depth + 1);
|
||||
|
||||
const shouldExplore =
|
||||
this.distanceFn === cosine ? true : Math.abs(diff) <= radius;
|
||||
this.distanceFn === cosineDistance ? true : Math.abs(diff) <= radius;
|
||||
|
||||
if (shouldExplore) {
|
||||
this.searchRadius(far, query, radius, results, depth + 1);
|
||||
@@ -309,7 +328,7 @@ export class KDTree<T = unknown> {
|
||||
|
||||
private collect(node: KDNode<T> | null, out: KDPoint<T>[]): void {
|
||||
if (node === null) return;
|
||||
out.push(node.point);
|
||||
if (!node.deleted) out.push(node.point);
|
||||
this.collect(node.left, out);
|
||||
this.collect(node.right, out);
|
||||
}
|
||||
@@ -0,0 +1,181 @@
|
||||
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;
|
||||
|
||||
export function memoryStore(memories: MemoryStore): {
|
||||
list: Memory[];
|
||||
cache: MemoryCache | null;
|
||||
find: (name: string) => Memory | undefined;
|
||||
ghosts: () => string[];
|
||||
search: (vector: number[], limit: number) => MemoryRef[];
|
||||
forget: (name: string) => boolean;
|
||||
rebuild: (changed?: Memory[]) => MemoryNode[];
|
||||
backfillEmbeddings: (llm: any) => Promise<number>;
|
||||
} {
|
||||
if(memories instanceof MemoryCache) {
|
||||
return {
|
||||
list: memories.memories,
|
||||
cache: memories,
|
||||
find: name => memories.find(name),
|
||||
ghosts: () => memories.ghosts(),
|
||||
search: (vector, limit) => memories.search(vector, limit),
|
||||
forget: name => memories.remove(name),
|
||||
rebuild: changed => memories.rebuild(changed),
|
||||
backfillEmbeddings: llm => memories.backfillEmbeddings(llm),
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
list: memories,
|
||||
cache: null,
|
||||
find: name => memories.find(m => m.name === name),
|
||||
ghosts: () => rebuildGraph(memories).filter(n => n.missing).map(n => n.name),
|
||||
search: (vector, limit) => memories
|
||||
.filter(m => m.embedding?.length)
|
||||
.map(m => ({
|
||||
name: m.name,
|
||||
description: m.description,
|
||||
distance: cosineDistance(vector, m.embedding),
|
||||
}))
|
||||
.sort((a, b) => a.distance - b.distance)
|
||||
.slice(0, limit),
|
||||
forget: name => {
|
||||
const idx = memories.findIndex(m => m.name === name);
|
||||
if(idx === -1) return false;
|
||||
|
||||
memories.splice(idx, 1);
|
||||
return true;
|
||||
},
|
||||
rebuild: changed => rebuildGraph(memories),
|
||||
backfillEmbeddings: async llm => {
|
||||
const missing = memories.filter(m => !m.embedding?.length);
|
||||
if(!missing.length) return 0;
|
||||
|
||||
await Promise.all(missing.map(async node => {
|
||||
await embedMemoryFields(node, llm);
|
||||
}));
|
||||
|
||||
return missing.length;
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
export class MemoryCache {
|
||||
private tree!: KDTree<MemoryRef>;
|
||||
private indexed = new Map<string, number[]>();
|
||||
public memories: Memory[];
|
||||
public nodes: MemoryNode[] = [];
|
||||
|
||||
get length() {
|
||||
return this.memories.length;
|
||||
}
|
||||
|
||||
constructor(memories: Memory[]) {
|
||||
this.memories = memories;
|
||||
this.tree = new KDTree<MemoryRef>(0);
|
||||
this.rebuild();
|
||||
}
|
||||
|
||||
find(name: string): Memory | undefined {
|
||||
return this.memories.find(m => m.name === name);
|
||||
}
|
||||
|
||||
private syncTree(): void {
|
||||
const current = new Set(this.memories.map(m => m.name));
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
|
||||
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(mem.embedding.length !== this.tree.dims) continue;
|
||||
|
||||
this.tree.insert({
|
||||
vector: mem.embedding,
|
||||
payload: {
|
||||
name: mem.name,
|
||||
description: mem.description,
|
||||
},
|
||||
});
|
||||
|
||||
this.indexed.set(mem.name, mem.embedding);
|
||||
}
|
||||
|
||||
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,
|
||||
}));
|
||||
}
|
||||
|
||||
add(memory: Memory): void {
|
||||
this.memories.push(memory);
|
||||
this.rebuild([memory]);
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
|
||||
ghosts(): string[] {
|
||||
return this.nodes.filter(n => n.missing).map(n => n.name);
|
||||
}
|
||||
|
||||
rebuild(changed?: Memory[]): MemoryNode[] {
|
||||
this.nodes = changed?.length && this.nodes.length
|
||||
? patchGraph(this.memories, this.nodes, changed)
|
||||
: rebuildGraph(this.memories);
|
||||
|
||||
this.syncTree();
|
||||
return this.nodes;
|
||||
}
|
||||
|
||||
commit(changed?: Memory[]): MemoryNode[] {
|
||||
return this.rebuild(changed);
|
||||
}
|
||||
|
||||
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;
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,348 @@
|
||||
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;
|
||||
description: string;
|
||||
content: string;
|
||||
embedding: number[];
|
||||
titleEmbedding?: number[];
|
||||
bodyEmbeddings?: number[][];
|
||||
links: string[];
|
||||
backlinks: string[];
|
||||
}
|
||||
|
||||
export type MemoryRef = {
|
||||
name: string;
|
||||
description: string;
|
||||
distance?: number;
|
||||
}
|
||||
|
||||
export type MemoryOptions = {
|
||||
memory: Memory[] | MemoryCache;
|
||||
inject?: boolean;
|
||||
tool?: boolean;
|
||||
update?: boolean;
|
||||
maxTokens?: number;
|
||||
}
|
||||
|
||||
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}]: ${h.content}`).join('\n\n').trim();
|
||||
if(!conversation) return store.list;
|
||||
|
||||
const memory = memoryTools(this.llm, memories);
|
||||
const memoryHistory: LLMMessage[] = [];
|
||||
|
||||
await this.llm.ask(conversation, {
|
||||
model: options.memoryModel || options.model,
|
||||
temperature: 0.2,
|
||||
maxTokens: options.maxTokens,
|
||||
tools: memory.tools,
|
||||
history: memoryHistory,
|
||||
system: this.memorySystem(memory.list),
|
||||
});
|
||||
|
||||
await this.reconcileSimilar(memoryHistory, memories, options);
|
||||
|
||||
const lastTurn = turns.at(-1);
|
||||
if(lastTurn) this.memorized.set(history, lastTurn);
|
||||
return store.list;
|
||||
}
|
||||
}
|
||||
+119
-35
@@ -25,25 +25,60 @@ export class OpenAi extends LLMProvider {
|
||||
return client;
|
||||
}
|
||||
|
||||
private toWireContent(content: any): any {
|
||||
if(!Array.isArray(content)) return content;
|
||||
return content.map(c => c.type === 'image'
|
||||
? {type: 'image_url', image_url: {url: `data:${c.mime};base64,${c.data}`}}
|
||||
: {type: 'text', text: c.text});
|
||||
}
|
||||
|
||||
/** Convert standard history -> OpenAI wire format */
|
||||
private toWire(history: LLMMessage[], system?: string): any[] {
|
||||
const wire: any[] = [];
|
||||
if(system) wire.push({role: 'system', content: system});
|
||||
for(const h of history) {
|
||||
if(h.role === 'tool') {
|
||||
|
||||
for(let i = 0; i < history.length; i++) {
|
||||
const h = history[i];
|
||||
|
||||
if(h.role !== 'tool') {
|
||||
wire.push({role: h.role, content: this.toWireContent(h.content)});
|
||||
continue;
|
||||
}
|
||||
|
||||
const calls: any[] = [];
|
||||
const results: any[] = [];
|
||||
|
||||
while(i < history.length && history[i].role === 'tool') {
|
||||
const tool: any = history[i];
|
||||
|
||||
calls.push({
|
||||
id: tool.id,
|
||||
type: 'function',
|
||||
function: {
|
||||
name: tool.name,
|
||||
arguments: JSON.stringify(tool.args || {})
|
||||
}
|
||||
});
|
||||
|
||||
results.push({
|
||||
role: 'tool',
|
||||
tool_call_id: tool.id,
|
||||
content: tool.error || tool.content || ''
|
||||
});
|
||||
|
||||
i++;
|
||||
}
|
||||
|
||||
wire.push({
|
||||
role: 'assistant',
|
||||
content: null,
|
||||
tool_calls: [{id: h.id, type: 'function', function: {name: h.name, arguments: JSON.stringify(h.args)}}],
|
||||
}, {
|
||||
role: 'tool',
|
||||
tool_call_id: h.id,
|
||||
content: h.error || h.content || '',
|
||||
tool_calls: calls
|
||||
});
|
||||
} else {
|
||||
wire.push({role: h.role, content: h.content});
|
||||
}
|
||||
|
||||
wire.push(...results);
|
||||
i--;
|
||||
}
|
||||
|
||||
return wire;
|
||||
}
|
||||
|
||||
@@ -53,13 +88,12 @@ export class OpenAi extends LLMProvider {
|
||||
if(!options.history) options.history = [];
|
||||
const history = options.history;
|
||||
if(message) history.push({role: 'user', content: message, timestamp: Date.now()});
|
||||
|
||||
const tools = options.tools || this.ai.options.llm?.tools || [];
|
||||
const requestParams: any = {
|
||||
model: options.model || this.model,
|
||||
stream: !!options.stream,
|
||||
max_completion_tokens: options.max_tokens || this.ai.options.llm?.max_tokens || undefined,
|
||||
temperature: options.temperature || this.ai.options.llm?.temperature || undefined,
|
||||
max_completion_tokens: options.maxTokens ?? this.ai.options.llm?.maxTokens,
|
||||
temperature: options.temperature ?? this.ai.options.llm?.temperature,
|
||||
tools: tools.map(t => ({
|
||||
type: 'function',
|
||||
function: {
|
||||
@@ -67,8 +101,12 @@ export class OpenAi extends LLMProvider {
|
||||
description: t.description,
|
||||
parameters: {
|
||||
type: 'object',
|
||||
properties: t.args ? objectMap(t.args, (key, value) => ({...value, required: undefined})) : {},
|
||||
required: t.args ? Object.entries(t.args).filter(t => t[1].required).map(t => t[0]) : []
|
||||
properties: t.args
|
||||
? objectMap(t.args, (key, value) => ({...value, required: undefined}))
|
||||
: {},
|
||||
required: t.args
|
||||
? Object.entries(t.args).filter(t => t[1].required).map(t => t[0])
|
||||
: []
|
||||
}
|
||||
}
|
||||
}))
|
||||
@@ -76,60 +114,106 @@ export class OpenAi extends LLMProvider {
|
||||
|
||||
if(options.schema) {
|
||||
const schema = convertSchema(options.schema);
|
||||
requestParams.response_format = {type: 'json_schema', json_schema: {name: 'response', strict: true, schema}};
|
||||
requestParams.response_format = {
|
||||
type: 'json_schema',
|
||||
json_schema: {name: 'response', strict: true, schema}
|
||||
};
|
||||
}
|
||||
if(options.stream) requestParams.stream_options = {include_usage: true};
|
||||
|
||||
try {
|
||||
let terminal = false;
|
||||
let iteration = 0;
|
||||
|
||||
do {
|
||||
iteration++;
|
||||
requestParams.messages = this.toWire(history.filter(h => h.role !== 'system'), options.system);
|
||||
|
||||
const callStart = Date.now();
|
||||
const resp: any = await this.tokenPool.run(token => this.getClient(token).chat.completions.create(requestParams)).catch(err => {
|
||||
const resp: any = await this.tokenPool.run(token =>
|
||||
this.getClient(token).chat.completions.create(requestParams)
|
||||
).catch(err => {
|
||||
err.message += `\n\nMessages:\n${JSON.stringify(requestParams.messages, null, 2)}`;
|
||||
throw err;
|
||||
});
|
||||
|
||||
let usage: any, msg: any = {content: '', tool_calls: []};
|
||||
let usage: any;
|
||||
let finishReason: string | undefined;
|
||||
let msg: any = {content: '', tool_calls: []};
|
||||
let streamedChars = 0;
|
||||
|
||||
if(options.stream) {
|
||||
let streamCompleted = false;
|
||||
try {
|
||||
for await (const chunk of resp) {
|
||||
if(controller.signal.aborted) break;
|
||||
if(chunk.usage) usage = chunk.usage;
|
||||
if(chunk.choices[0]?.delta?.content) {
|
||||
msg.content += chunk.choices[0].delta.content;
|
||||
options.stream({text: chunk.choices[0].delta.content});
|
||||
|
||||
const choice = chunk.choices?.[0];
|
||||
if(choice?.finish_reason) finishReason = choice.finish_reason;
|
||||
|
||||
if(choice?.delta?.content) {
|
||||
msg.content += choice.delta.content;
|
||||
streamedChars += choice.delta.content.length;
|
||||
options.stream({text: choice.delta.content});
|
||||
}
|
||||
if(chunk.choices[0]?.delta?.tool_calls) {
|
||||
for(const deltaTC of chunk.choices[0].delta.tool_calls) {
|
||||
const existing = msg.tool_calls.find((tc: any) => tc.index === deltaTC.index);
|
||||
if(existing) {
|
||||
|
||||
if(choice?.delta?.tool_calls) {
|
||||
for(const deltaTC of choice.delta.tool_calls) {
|
||||
const index = deltaTC.index ?? msg.tool_calls.length;
|
||||
let existing = msg.tool_calls.find((tc: any) => tc.index === index);
|
||||
|
||||
if(!existing) {
|
||||
existing = {index, id: '', function: {name: '', arguments: ''}};
|
||||
msg.tool_calls.push(existing);
|
||||
}
|
||||
|
||||
if(deltaTC.id) existing.id = deltaTC.id;
|
||||
if(deltaTC.function?.name) existing.function.name = deltaTC.function.name;
|
||||
if(deltaTC.function?.arguments) existing.function.arguments += deltaTC.function.arguments;
|
||||
} else {
|
||||
msg.tool_calls.push({
|
||||
index: deltaTC.index,
|
||||
id: deltaTC.id || '',
|
||||
function: {name: deltaTC.function?.name || '', arguments: deltaTC.function?.arguments || ''}
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
streamCompleted = true;
|
||||
} catch(err) {
|
||||
if(!controller.signal.aborted) throw err;
|
||||
}
|
||||
|
||||
if(streamCompleted && !finishReason) finishReason = msg.tool_calls.length ? 'tool_calls' : 'stop';
|
||||
} else {
|
||||
usage = resp.usage;
|
||||
finishReason = resp.choices[0].finish_reason;
|
||||
msg = resp.choices[0].message;
|
||||
}
|
||||
|
||||
const duration = Date.now() - callStart;
|
||||
const tps = usage?.completion_tokens && duration > 0 ? usage.completion_tokens / (duration / 1000) : 0;
|
||||
|
||||
if(finishReason === 'length' && !controller.signal.aborted) {
|
||||
if(msg.content?.trim()) history.push({role: 'assistant', content: msg.content.trim(), timestamp: Date.now(), duration, tps});
|
||||
throw new Error(`[OpenAI] Response hit token limit before completing`);
|
||||
}
|
||||
|
||||
if(!finishReason && !controller.signal.aborted) {
|
||||
throw new Error('[OpenAI] Completion ended without a usable response');
|
||||
}
|
||||
|
||||
const toolCalls = msg.tool_calls || [];
|
||||
|
||||
if(toolCalls.length && !controller.signal.aborted) {
|
||||
if(msg.content?.trim()) history.push({role: 'assistant', content: msg.content.trim(), timestamp: Date.now(), duration, tps});
|
||||
|
||||
const entries = toolCalls.map((tc: any) => {
|
||||
const entry: any = {role: 'tool', id: tc.id, name: tc.function.name, args: JSONAttemptParse(tc.function.arguments, {}), content: undefined, timestamp: Date.now()};
|
||||
const entry: any = {
|
||||
role: 'tool',
|
||||
id: tc.id,
|
||||
name: tc.function.name,
|
||||
args: JSONAttemptParse(tc.function.arguments, {}),
|
||||
content: undefined,
|
||||
timestamp: Date.now()
|
||||
};
|
||||
|
||||
history.push(entry);
|
||||
return {tc, entry};
|
||||
});
|
||||
@@ -137,12 +221,13 @@ export class OpenAi extends LLMProvider {
|
||||
await Promise.all(entries.map(async ({tc, entry}: any) => {
|
||||
const tool = tools.find(findByProp('name', tc.function.name));
|
||||
if(options.stream) options.stream({tool: tc.function.name});
|
||||
if(!tool) { entry.error = 'Tool not found'; return; }
|
||||
if(!tool) return entry.error = 'Tool not found';
|
||||
try {
|
||||
const toolStream = options.stream && ((chunk: any) => {
|
||||
if(chunk.done) { terminal = true; return; }
|
||||
if(chunk.done) return;
|
||||
options.stream!(chunk);
|
||||
});
|
||||
|
||||
const result = await tool.fn(entry.args, toolStream, this.ai, tc.id);
|
||||
entry.content = typeof result === 'object' ? JSONSanitize(result) : result;
|
||||
} catch(err: any) {
|
||||
@@ -157,7 +242,6 @@ export class OpenAi extends LLMProvider {
|
||||
} while(!terminal && !controller.signal.aborted);
|
||||
|
||||
if(options.stream) options.stream({done: true});
|
||||
|
||||
const turnStart = history.map(h => h.role).lastIndexOf('user');
|
||||
const finalContent = history.slice(turnStart + 1).reduce((str, h) => h.role === 'assistant' ? str + (h.content || '') : str, '').trim();
|
||||
res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
|
||||
|
||||
@@ -0,0 +1,82 @@
|
||||
import {Memory} from './memory/memory.ts';
|
||||
|
||||
export 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;
|
||||
}
|
||||
|
||||
export async function embedMemoryFields(node: Memory, llm: any): Promise<void> {
|
||||
const body = stripHeader(node.content);
|
||||
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);
|
||||
}
|
||||
|
||||
export function euclideanDistance(a: number[], b: number[]): number {
|
||||
let sum = 0;
|
||||
for(let i = 0; i < a.length; i++) {
|
||||
const d = a[i] - b[i];
|
||||
sum += d * d;
|
||||
}
|
||||
return Math.sqrt(sum);
|
||||
}
|
||||
|
||||
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;
|
||||
d.setUTCDate(d.getUTCDate() + diff);
|
||||
return d.toISOString().slice(0, 10);
|
||||
}
|
||||
|
||||
export function journalDescription(journalName?: string): string {
|
||||
const start = journalName?.split('/').pop() || getWeekStart();
|
||||
const d = new Date(`${start}T00:00:00Z`);
|
||||
d.setUTCDate(d.getUTCDate() + 6);
|
||||
const end = d.toISOString().slice(0, 10);
|
||||
return `Log from ${start} - ${end}`;
|
||||
}
|
||||
|
||||
function parseFrontmatter(content: string): {fm: Map<string, string>, body: string} {
|
||||
const match = content.match(/^---\n([\s\S]*?)\n---\n?([\s\S]*)$/);
|
||||
if(!match) return {fm: new Map(), body: content};
|
||||
const fm = new Map<string, string>();
|
||||
for(const line of match[1].split('\n')) {
|
||||
const i = line.indexOf(':');
|
||||
if(i === -1) continue;
|
||||
const key = line.slice(0, i).trim();
|
||||
const raw = line.slice(i + 1).trim();
|
||||
let value = raw;
|
||||
try { value = JSON.parse(raw); } catch { }
|
||||
fm.set(key, value);
|
||||
}
|
||||
return {fm, body: match[2]};
|
||||
}
|
||||
|
||||
export function writeFrontmatter(fm: Map<string, string>, body: string): string {
|
||||
const lines = [...fm.entries()].map(([k, v]) =>
|
||||
`${k}: ${JSON.stringify(String(v).replace(/\s+/g, ' ').trim())}`);
|
||||
return `---\n${lines.join('\n')}\n---\n\n${body.trimStart()}`;
|
||||
}
|
||||
|
||||
export function stripHeader(content: string): string {
|
||||
return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
|
||||
}
|
||||
|
||||
export function updateMemory(node: Memory, body: string): string {
|
||||
const {fm} = parseFrontmatter(node.content);
|
||||
fm.set('name', node.name);
|
||||
fm.set('description', (node.name.startsWith('Journal/') ? journalDescription(node.name) : node.description)
|
||||
|| 'Persistent memory document');
|
||||
fm.set('modified', new Date().toISOString());
|
||||
return writeFrontmatter(fm, stripHeader(body));
|
||||
}
|
||||
@@ -1,167 +0,0 @@
|
||||
|
||||
import {describe, it, expect, vi, beforeEach} from 'vitest';
|
||||
import LLM from '../src/llm';
|
||||
|
||||
const {FakeProvider, providerLog} = vi.hoisted(() => {
|
||||
const providerLog: any[] = [];
|
||||
class FakeProvider {
|
||||
model: string;
|
||||
constructor(...args: any[]) { this.model = args[args.length - 1]; }
|
||||
ask(message: string, opts: any) {
|
||||
let aborted = false;
|
||||
const p = (async () => {
|
||||
const script = (globalThis as any).__scripts?.[this.model];
|
||||
const plan = script ? script(message, opts) : {text: ''};
|
||||
providerLog.push({model: this.model, message, system: opts.system, tools: (opts.tools || []).map((t: any) => t.name)});
|
||||
for (const c of plan.calls || []) {
|
||||
if (aborted) break;
|
||||
const tool = (opts.tools || []).find((t: any) => t.name === c.tool);
|
||||
const id = c.id || `${c.tool}_${Math.random()}`;
|
||||
const content = await tool.fn(c.args, opts.stream, null, id);
|
||||
opts.history.push({role: 'tool', id, name: c.tool, args: c.args, content, timestamp: Date.now()});
|
||||
}
|
||||
const text = plan.text ?? '';
|
||||
if (opts.stream && text) opts.stream({text, done: true});
|
||||
opts.history.push({role: 'assistant', content: text, timestamp: Date.now(), duration: 10, tps: 5});
|
||||
return text;
|
||||
})();
|
||||
return Object.assign(p, {abort: () => { aborted = true; }});
|
||||
}
|
||||
}
|
||||
return {FakeProvider, providerLog};
|
||||
});
|
||||
|
||||
vi.mock('../src/antrhopic.ts', () => ({Anthropic: FakeProvider}));
|
||||
vi.mock('../src/open-ai.ts', () => ({OpenAi: FakeProvider}));
|
||||
|
||||
function makeAi(models: any) {
|
||||
return {options: {llm: {models}}} as any;
|
||||
}
|
||||
|
||||
beforeEach(() => {
|
||||
providerLog.length = 0;
|
||||
(globalThis as any).__scripts = {};
|
||||
});
|
||||
|
||||
describe('LLM cross-provider interchangeability', () => {
|
||||
it('runs identical tool calls the same way on an anthropic-backed model and an openai-backed model', async () => {
|
||||
const ai = makeAi({
|
||||
claude: {proto: 'anthropic', token: 'x'},
|
||||
gpt: {proto: 'openai', token: 'y', host: 'http://local'},
|
||||
});
|
||||
const llm = new LLM(ai);
|
||||
const calc = {
|
||||
name: 'calc_add',
|
||||
description: 'Add two numbers',
|
||||
args: {a: {type: 'number', required: true}, b: {type: 'number', required: true}},
|
||||
fn: (args: any) => String(args.a + args.b),
|
||||
};
|
||||
|
||||
(globalThis as any).__scripts.claude = () => ({calls: [{tool: 'calc_add', args: {a: 2, b: 3}}], text: 'Result: 5'});
|
||||
(globalThis as any).__scripts.gpt = () => ({calls: [{tool: 'calc_add', args: {a: 2, b: 3}}], text: 'Result: 5'});
|
||||
|
||||
const historyA: any[] = [], historyB: any[] = [];
|
||||
const respA = await llm.ask('add 2 and 3', {model: 'claude', tools: [calc], history: historyA});
|
||||
const respB = await llm.ask('add 2 and 3', {model: 'gpt', tools: [calc], history: historyB});
|
||||
|
||||
expect(respA).toBe('Result: 5');
|
||||
expect(respB).toBe('Result: 5');
|
||||
expect(providerLog.find(l => l.model === 'claude')!.tools).toContain('calc_add');
|
||||
expect(providerLog.find(l => l.model === 'gpt')!.tools).toContain('calc_add');
|
||||
|
||||
// tool timing gets recomputed from real execution regardless of proto
|
||||
for (const h of [historyA.find(h => h.name === 'calc_add'), historyB.find(h => h.name === 'calc_add')]) {
|
||||
expect(h.content).toBe('5');
|
||||
expect(typeof h.duration).toBe('number');
|
||||
expect(typeof h.tps).toBe('number');
|
||||
}
|
||||
});
|
||||
|
||||
it('lets the same shared history flow across model + proto swaps with different system prompts', async () => {
|
||||
const ai = makeAi({
|
||||
claude: {proto: 'anthropic', token: 'x'},
|
||||
gpt: {proto: 'openai', token: 'y', host: 'http://local'},
|
||||
});
|
||||
const llm = new LLM(ai);
|
||||
const history: any[] = [];
|
||||
|
||||
(globalThis as any).__scripts.claude = () => ({text: 'Hi from claude'});
|
||||
(globalThis as any).__scripts.gpt = () => ({text: 'Hi from gpt'});
|
||||
|
||||
const r1 = await llm.ask('hello', {model: 'claude', system: 'You are terse.', history});
|
||||
const r2 = await llm.ask('follow up', {model: 'gpt', system: 'You are verbose.', history});
|
||||
|
||||
expect(r1).toBe('Hi from claude');
|
||||
expect(r2).toBe('Hi from gpt');
|
||||
expect(history.filter(h => h.role === 'assistant').map(h => h.content)).toEqual(['Hi from claude', 'Hi from gpt']);
|
||||
expect(providerLog[0].system).toContain('You are terse.');
|
||||
expect(providerLog[1].system).toContain('You are verbose.');
|
||||
});
|
||||
|
||||
it('exposes MCP tools the same way no matter which proto backs the model', async () => {
|
||||
const ai = makeAi({claude: {proto: 'anthropic', token: 'x'}, gpt: {proto: 'openai', token: 'y', host: 'http://local'}});
|
||||
const llm = new LLM(ai);
|
||||
const mcp = [{name: 'weather', host: 'http://mcp.local'}];
|
||||
|
||||
global.fetch = vi.fn(async (url: string, opts?: any) => {
|
||||
if (url.endsWith('/tools')) {
|
||||
return {json: async () => ({tools: [{name: 'lookup', description: 'Look up weather', inputSchema: {properties: {city: {type: 'string'}}, required: ['city']}}]})} as any;
|
||||
}
|
||||
const body = JSON.parse(opts.body);
|
||||
return {json: async () => ({content: [{text: `Sunny in ${body.arguments.city}`}]})} as any;
|
||||
}) as any;
|
||||
|
||||
for (const model of ['claude', 'gpt']) {
|
||||
(globalThis as any).__scripts[model] = () => ({calls: [{tool: 'weather_lookup', args: {city: 'Rome'}}], text: 'done'});
|
||||
const history: any[] = [];
|
||||
await llm.ask('weather?', {model, mcp, history});
|
||||
expect(history.find(h => h.name === 'weather_lookup')?.content).toBe('Sunny in Rome');
|
||||
}
|
||||
});
|
||||
|
||||
it('exposes and resolves skill documents identically across protos', async () => {
|
||||
const ai = makeAi({claude: {proto: 'anthropic', token: 'x'}, gpt: {proto: 'openai', token: 'y', host: 'http://local'}});
|
||||
const llm = new LLM(ai);
|
||||
const skills = [{name: 'Onboarding', description: 'How to onboard a user', content: 'Step 1...'}];
|
||||
|
||||
for (const model of ['claude', 'gpt']) {
|
||||
(globalThis as any).__scripts[model] = () => ({calls: [{tool: 'skill_read', args: {name: 'Onboarding'}}], text: 'done'});
|
||||
const history: any[] = [];
|
||||
await llm.ask('onboard me', {model, skills, history});
|
||||
expect(history.find(h => h.name === 'skill_read')?.content).toContain('Step 1...');
|
||||
}
|
||||
});
|
||||
|
||||
it('delegate agent mutates the shared history directly and backfills the orchestrator response, across protos', async () => {
|
||||
const ai = makeAi({claude: {proto: 'anthropic', token: 'x'}, gpt: {proto: 'openai', token: 'y', host: 'http://local'}});
|
||||
const llm = new LLM(ai);
|
||||
const history: any[] = [{role: 'user', content: 'research quantum computing'}];
|
||||
const researcher = {name: 'researcher', system: 'You research topics.', delegate: true, model: 'gpt'};
|
||||
|
||||
(globalThis as any).__scripts.claude = () => ({calls: [{tool: 'agent_researcher', args: {}}], text: ''});
|
||||
(globalThis as any).__scripts.gpt = () => ({text: 'Quantum computers use qubits.'});
|
||||
|
||||
const resp = await llm.ask('go', {model: 'claude', agents: [researcher], history});
|
||||
|
||||
expect(resp).toBe('Quantum computers use qubits.');
|
||||
expect(history.some(h => h.role === 'assistant' && h.content === 'Quantum computers use qubits.')).toBe(true);
|
||||
expect(history.find(h => h.name === 'agent_researcher')?.content).toBe('');
|
||||
});
|
||||
|
||||
it('regular (non-delegate) subagent keeps its own isolated history separate from the parent, across protos', async () => {
|
||||
const ai = makeAi({claude: {proto: 'anthropic', token: 'x'}, gpt: {proto: 'openai', token: 'y', host: 'http://local'}});
|
||||
const llm = new LLM(ai);
|
||||
const history: any[] = [];
|
||||
const summarizer = {name: 'summarizer', system: 'You summarize text.', model: 'gpt'};
|
||||
|
||||
(globalThis as any).__scripts.claude = () => ({calls: [{tool: 'subagent_summarizer', args: {context: 'a long article', instructions: 'summarize it'}}], text: 'Summary: short version'});
|
||||
(globalThis as any).__scripts.gpt = () => ({text: 'short version'});
|
||||
|
||||
const resp = await llm.ask('summarize this', {model: 'claude', agents: [summarizer], history});
|
||||
|
||||
expect(resp).toBe('Summary: short version');
|
||||
expect(history.find(h => h.name === 'subagent_summarizer')?.content).toBe('short version');
|
||||
// isolated history - subagent's own assistant turn never leaks into the parent
|
||||
expect(history.some(h => h.role === 'assistant' && h.content === 'short version')).toBe(false);
|
||||
});
|
||||
});
|
||||
@@ -1,256 +0,0 @@
|
||||
import {describe, it, expect, vi, beforeEach} from 'vitest';
|
||||
import {MemoryManager, MemoryCache, rebuildGraph, Memory} from '../src/memory';
|
||||
|
||||
function makeMemory(overrides: Partial<Memory> = {}): Memory {
|
||||
return {
|
||||
name: 'Test/Doc',
|
||||
description: '',
|
||||
content: '',
|
||||
embedding: [],
|
||||
links: [],
|
||||
backlinks: [],
|
||||
...overrides,
|
||||
};
|
||||
}
|
||||
|
||||
function makeLLM() {
|
||||
return {
|
||||
embedding: vi.fn(async (_text: string) => [{embedding: [1, 0, 0]}]),
|
||||
ask: vi.fn(async () => undefined),
|
||||
};
|
||||
}
|
||||
|
||||
describe('rebuildGraph', () => {
|
||||
it('extracts [[WikiLinks]] from content, excluding self-links', () => {
|
||||
const a = makeMemory({name: 'A', content: '[[B]] and [[A]] and [[C]]'});
|
||||
const b = makeMemory({name: 'B', content: 'no links here'});
|
||||
const mem = [a, b];
|
||||
|
||||
rebuildGraph(mem);
|
||||
|
||||
expect(a.links).toEqual(['B', 'C']);
|
||||
expect(b.links).toEqual([]);
|
||||
});
|
||||
|
||||
it('computes backlinks only for links that resolve to a real node', () => {
|
||||
const a = makeMemory({name: 'A', content: '[[B]] [[Missing]]'});
|
||||
const b = makeMemory({name: 'B', content: ''});
|
||||
const mem = [a, b];
|
||||
|
||||
rebuildGraph(mem);
|
||||
|
||||
expect(b.backlinks).toEqual(['A']);
|
||||
expect(mem.find(m => m.name === 'Missing')).toBeUndefined();
|
||||
});
|
||||
|
||||
it('resets stale backlinks on every rebuild (no leftover from a removed link)', () => {
|
||||
const a = makeMemory({name: 'A', content: '[[B]]'});
|
||||
const b = makeMemory({name: 'B', content: ''});
|
||||
const mem = [a, b];
|
||||
rebuildGraph(mem);
|
||||
expect(b.backlinks).toEqual(['A']);
|
||||
|
||||
a.content = 'no more links';
|
||||
rebuildGraph(mem);
|
||||
expect(b.backlinks).toEqual([]);
|
||||
});
|
||||
});
|
||||
|
||||
describe('MemoryCache', () => {
|
||||
it('finds nearest neighbor by embedding via KD-tree search', () => {
|
||||
const close = makeMemory({name: 'Close', embedding: [1, 0, 0]});
|
||||
const far = makeMemory({name: 'Far', embedding: [0, 0, 1]});
|
||||
const cache = new MemoryCache([close, far]);
|
||||
|
||||
const results = cache.search([1, 0, 0], 1);
|
||||
|
||||
expect(results[0].name).toBe('Close');
|
||||
});
|
||||
|
||||
it('rebuilds the tree on add/update/remove', () => {
|
||||
const cache = new MemoryCache([makeMemory({name: 'A', embedding: [1, 0, 0]})]);
|
||||
cache.add(makeMemory({name: 'B', embedding: [0, 1, 0]}));
|
||||
expect(cache.search([0, 1, 0], 1)[0].name).toBe('B');
|
||||
|
||||
cache.remove('B');
|
||||
expect(cache.search([0, 1, 0], 1)[0]?.name).not.toBe('B');
|
||||
});
|
||||
});
|
||||
|
||||
describe('MemoryManager.forget', () => {
|
||||
it('removes the node and recomputes backlinks for the rest of the graph', () => {
|
||||
const llm = makeLLM();
|
||||
const mgr = new MemoryManager(llm);
|
||||
const a = makeMemory({name: 'A', content: '[[B]]'});
|
||||
const b = makeMemory({name: 'B', content: '[[C]]'});
|
||||
const c = makeMemory({name: 'C', content: ''});
|
||||
const mem = [a, b, c];
|
||||
rebuildGraph(mem);
|
||||
expect(c.backlinks).toEqual(['B']);
|
||||
|
||||
const ok = mgr.forget('B', mem);
|
||||
|
||||
expect(ok).toBe(true);
|
||||
expect(mem.find(m => m.name === 'B')).toBeUndefined();
|
||||
expect(a.links).toEqual(['B']);
|
||||
expect(c.backlinks).toEqual([]);
|
||||
});
|
||||
|
||||
it('returns false for an unknown name', () => {
|
||||
const mgr = new MemoryManager(makeLLM());
|
||||
expect(mgr.forget('Nope', [makeMemory({name: 'A'})])).toBe(false);
|
||||
});
|
||||
});
|
||||
|
||||
describe('MemoryManager.recollect', () => {
|
||||
it('orders vector matches first, then expands one hop via links', async () => {
|
||||
const llm = makeLLM();
|
||||
llm.embedding.mockResolvedValue([{embedding: [1, 0, 0]}]);
|
||||
const mgr = new MemoryManager(llm);
|
||||
|
||||
const near = makeMemory({name: 'Near', embedding: [1, 0, 0], content: '[[Linked]]'});
|
||||
const linked = makeMemory({name: 'Linked', embedding: [0, 0, 1], content: ''});
|
||||
const far = makeMemory({name: 'Far', embedding: [0, 1, 0], content: ''});
|
||||
const mem = [near, linked, far];
|
||||
rebuildGraph(mem);
|
||||
|
||||
const result = await mgr.recollect('query', mem, 1, 1);
|
||||
|
||||
expect(result.map(r => r.name)).toEqual(['Near', 'Linked']);
|
||||
});
|
||||
|
||||
it('returns [] when there are no memories', async () => {
|
||||
const mgr = new MemoryManager(makeLLM());
|
||||
expect(await mgr.recollect('q', [])).toEqual([]);
|
||||
});
|
||||
});
|
||||
|
||||
describe('MemoryManager.memorize (fast path)', () => {
|
||||
let llm: ReturnType<typeof makeLLM>;
|
||||
let mgr: MemoryManager;
|
||||
|
||||
beforeEach(() => {
|
||||
llm = makeLLM();
|
||||
mgr = new MemoryManager(llm);
|
||||
});
|
||||
|
||||
it('pushes a pending tool message, then resolves it to links once facts land', async () => {
|
||||
llm.ask.mockImplementation(async (_prompt: string, opts: any) => {
|
||||
if (opts.tools) {
|
||||
opts.tools[0].fn({destination: 'Projects/Oxide', facts: 'Uses a hybrid memory system'});
|
||||
return undefined;
|
||||
}
|
||||
return {description: 'd', content: '# doc'};
|
||||
});
|
||||
|
||||
const history: any[] = [{role: 'user', content: 'we use a hybrid memory system'}];
|
||||
const touched = await mgr.memorize(history, [], {model: 'test'} as any);
|
||||
|
||||
const pending = history.find(h => h.name === 'memory_process');
|
||||
expect(pending).toBeDefined();
|
||||
expect(pending.content).toContain('[[Projects/Oxide]]');
|
||||
expect(touched.map(t => t.name)).toEqual(['Projects/Oxide']);
|
||||
});
|
||||
|
||||
it('creates a new node and appends facts under "## Facts" without calling the doc LLM', async () => {
|
||||
llm.ask.mockImplementation(async (_prompt: string, opts: any) => {
|
||||
if (opts.tools) opts.tools[0].fn({destination: 'People/Sarah', facts: 'Works at Acme, Likes hiking'});
|
||||
return undefined;
|
||||
});
|
||||
|
||||
const mem: Memory[] = [];
|
||||
await mgr.memorize([{role: 'user', content: 'Sarah works at Acme and likes hiking'}] as any, mem, {model: 'test'} as any);
|
||||
|
||||
const node = mem.find(m => m.name === 'People/Sarah')!;
|
||||
expect(node).toBeDefined();
|
||||
expect(node.content).toContain('## Facts');
|
||||
expect(node.content).toContain('- Works at Acme');
|
||||
expect(node.content).toContain('- Likes hiking');
|
||||
// doc reconciler LLM (schema call) should NOT have been awaited synchronously in this fast path assertion
|
||||
});
|
||||
|
||||
it('routes "journal" destination to Journal/{weekMonday}', async () => {
|
||||
llm.ask.mockImplementation(async (_prompt: string, opts: any) => {
|
||||
if (opts.tools) opts.tools[0].fn({destination: 'journal', facts: 'Shipped v1'});
|
||||
return undefined;
|
||||
});
|
||||
|
||||
const mem: Memory[] = [];
|
||||
const touched = await mgr.memorize([{role: 'user', content: 'shipped v1 today'}] as any, mem, {model: 'test'} as any);
|
||||
|
||||
expect(touched[0].name).toMatch(/^Journal\/\d{4}-\d{2}-\d{2}$/);
|
||||
});
|
||||
|
||||
it('reports nothing to remember when no facts are extracted', async () => {
|
||||
llm.ask.mockResolvedValue(undefined); // tools present but fn never called
|
||||
|
||||
const history: any[] = [{role: 'user', content: 'hey'}];
|
||||
const touched = await mgr.memorize(history, [], {model: 'test'} as any);
|
||||
|
||||
expect(touched).toEqual([]);
|
||||
expect(history.find(h => h.name === 'memory_process').content).toBe('Nothing worth remembering.');
|
||||
});
|
||||
|
||||
it('returns [] and does nothing for an empty conversation', async () => {
|
||||
const touched = await mgr.memorize([], [], {model: 'test'} as any);
|
||||
expect(touched).toEqual([]);
|
||||
expect(llm.ask).not.toHaveBeenCalled();
|
||||
});
|
||||
});
|
||||
|
||||
describe('MemoryManager reconcileVault', () => {
|
||||
it('integrates the "## Facts" section via the doc LLM and removes it', async () => {
|
||||
const llm = makeLLM();
|
||||
llm.ask.mockResolvedValue({description: 'Tidy summary', content: '# Doc\n\nIntegrated fact.'});
|
||||
const mgr = new MemoryManager(llm);
|
||||
|
||||
const node = makeMemory({
|
||||
name: 'Projects/Oxide',
|
||||
content: '---\nname: Projects/Oxide\n---\n\n# Doc\n\n## Facts\n- some raw fact\n',
|
||||
});
|
||||
const mem = [node];
|
||||
|
||||
await mgr.reconcileVault(mem, {model: 'test'} as any, 'all');
|
||||
|
||||
expect(node.content).not.toContain('## Facts');
|
||||
expect(node.content).toContain('Integrated fact.');
|
||||
expect(node.description).toBe('Tidy summary');
|
||||
});
|
||||
|
||||
it('only targets docs with a pending Facts inbox when scope is "touched"', async () => {
|
||||
const llm = makeLLM();
|
||||
llm.ask.mockResolvedValue({description: 'd', content: '# clean'});
|
||||
const mgr = new MemoryManager(llm);
|
||||
|
||||
const dirty = makeMemory({name: 'A', content: '## Facts\n- x'});
|
||||
const clean = makeMemory({name: 'B', content: '# already tidy'});
|
||||
await mgr.reconcileVault([dirty, clean], {model: 'test'} as any, 'touched');
|
||||
|
||||
expect(dirty.content).toContain('# clean'); // rewritten (frontmatter now wraps it)
|
||||
expect(clean.content).toBe('# already tidy'); // untouched, never queued
|
||||
});
|
||||
});
|
||||
|
||||
describe('MemoryManager reconcile coalescing', () => {
|
||||
it('coalesces a second call while one is in-flight: marks dirty, aborts, reuses the same task promise', () => {
|
||||
const llm = makeLLM();
|
||||
const abort = vi.fn();
|
||||
let calls = 0;
|
||||
llm.ask.mockImplementation(() => {
|
||||
calls++;
|
||||
const pending: any = new Promise(() => {}); // never resolves in this test
|
||||
pending.abort = abort;
|
||||
return pending;
|
||||
});
|
||||
const mgr: any = new MemoryManager(llm);
|
||||
const node = makeMemory({name: 'Q', content: '# Q\n\n## Facts\n- f'});
|
||||
const mem = [node];
|
||||
|
||||
const p1 = mgr.reconcile(node, mem, {model: 'test'});
|
||||
const p2 = mgr.reconcile(node, mem, {model: 'test'});
|
||||
|
||||
expect(p2).toBe(p1); // same in-flight task, not a new queue entry
|
||||
expect(abort).toHaveBeenCalledTimes(1); // second call aborted the in-flight request
|
||||
expect(calls).toBe(1); // no second ask() fired synchronously — it'll rerun via the dirty loop
|
||||
});
|
||||
});
|
||||
Reference in New Issue
Block a user