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21 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 497f051c62 | |||
| 62fbe73b22 | |||
| d53b1c6328 | |||
| 89619e211e | |||
| afc6653364 | |||
| 68e72445a2 | |||
| 1aa6cdf329 | |||
| d022a5ef4d | |||
| a1d438a20a | |||
| 52a9e3aaa4 | |||
| a7aec4ee29 | |||
| dda2d4c2a3 | |||
| 58e0e488e4 | |||
| 8dfcd06752 | |||
| 14f6cdd313 | |||
| 73d6ee0f2a | |||
| bee4085666 | |||
| 3b5c71de7c | |||
| 8229e02a52 | |||
| a6fb8ae828 | |||
| d1230bcaad |
234
package-lock.json
generated
234
package-lock.json
generated
@@ -1,12 +1,12 @@
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|||||||
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@@ -2565,9 +2605,9 @@
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@@ -3092,9 +3132,9 @@
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@@ -3232,9 +3272,9 @@
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@@ -3252,7 +3292,7 @@
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@@ -3787,9 +3827,9 @@
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@@ -3981,16 +4021,16 @@
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@@ -1,6 +1,6 @@
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|
||||||
"description": "AI Utility library",
|
"description": "AI Utility library",
|
||||||
"author": "Zak Timson",
|
"author": "Zak Timson",
|
||||||
"license": "MIT",
|
"license": "MIT",
|
||||||
|
|||||||
@@ -21,10 +21,10 @@ export class Anthropic extends LLMProvider {
|
|||||||
messages.push(<any>{timestamp, ...h});
|
messages.push(<any>{timestamp, ...h});
|
||||||
} else {
|
} else {
|
||||||
const textContent = h.content?.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
|
const textContent = h.content?.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
|
||||||
if(textContent) messages.push({timestamp, role: h.role, content: textContent});
|
if(textContent) messages.push({role: h.role, content: textContent, timestamp: timestamp, duration: h.duration, tps: h.tps});
|
||||||
h.content.forEach((c: any) => {
|
h.content.forEach((c: any) => {
|
||||||
if(c.type == 'tool_use') {
|
if(c.type == 'tool_use') {
|
||||||
messages.push({timestamp, role: 'tool', id: c.id, name: c.name, args: c.input, content: undefined});
|
messages.push({role: 'tool', id: c.id, name: c.name, args: c.input, timestamp: h.timestamp, content: undefined, duration: h.duration, tps: h.tps});
|
||||||
} else if(c.type == 'tool_result') {
|
} else if(c.type == 'tool_result') {
|
||||||
const m: any = messages.findLast(m => (<any>m).id == c.tool_use_id);
|
const m: any = messages.findLast(m => (<any>m).id == c.tool_use_id);
|
||||||
if(m) m[c.is_error ? 'error' : 'content'] = c.content;
|
if(m) m[c.is_error ? 'error' : 'content'] = c.content;
|
||||||
@@ -46,13 +46,16 @@ export class Anthropic extends LLMProvider {
|
|||||||
i++;
|
i++;
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
return history.map(({timestamp, ...h}) => h);
|
return history;
|
||||||
}
|
}
|
||||||
|
|
||||||
ask(message: string, options: LLMRequest = {}): AbortablePromise<string | any> {
|
ask(message: string, options: LLMRequest = {}): AbortablePromise<string | any> {
|
||||||
const controller = new AbortController();
|
const controller = new AbortController();
|
||||||
return Object.assign(new Promise<any>(async (res) => {
|
return Object.assign(new Promise<any>(async (res) => {
|
||||||
let history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
|
let history = this.fromStandard([
|
||||||
|
...(options.history || []).filter(h => h.role !== 'system'),
|
||||||
|
{role: 'user', content: message, timestamp: Date.now()}
|
||||||
|
]);
|
||||||
const tools = options.tools || this.ai.options.llm?.tools || [];
|
const tools = options.tools || this.ai.options.llm?.tools || [];
|
||||||
const requestParams: any = {
|
const requestParams: any = {
|
||||||
model: options.model || this.model,
|
model: options.model || this.model,
|
||||||
@@ -83,17 +86,17 @@ export class Anthropic extends LLMProvider {
|
|||||||
};
|
};
|
||||||
}
|
}
|
||||||
|
|
||||||
let resp: any, isFirstMessage = true;
|
let resp: any, terminal = false, duration = 0, tps = 0;
|
||||||
do {
|
do {
|
||||||
|
requestParams.messages = history.map(({timestamp, ...m}) => m);
|
||||||
|
const callStart = Date.now();
|
||||||
resp = await this.client.messages.create(requestParams).catch(err => {
|
resp = await this.client.messages.create(requestParams).catch(err => {
|
||||||
err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
|
err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
|
||||||
throw err;
|
throw err;
|
||||||
});
|
});
|
||||||
|
|
||||||
// Streaming mode
|
let usage: any;
|
||||||
if(options.stream) {
|
if(options.stream) {
|
||||||
if(!isFirstMessage) options.stream({text: '\n\n'});
|
|
||||||
else isFirstMessage = false;
|
|
||||||
resp.content = [];
|
resp.content = [];
|
||||||
for await (const chunk of resp) {
|
for await (const chunk of resp) {
|
||||||
if(controller.signal.aborted) break;
|
if(controller.signal.aborted) break;
|
||||||
@@ -113,42 +116,57 @@ export class Anthropic extends LLMProvider {
|
|||||||
}
|
}
|
||||||
} else if(chunk.type === 'content_block_stop') {
|
} else if(chunk.type === 'content_block_stop') {
|
||||||
const last = resp.content.at(-1);
|
const last = resp.content.at(-1);
|
||||||
if(last.input != null) last.input = last.input ? JSONAttemptParse(last.input, {}) : {};
|
if(last?.input != null) last.input = last.input ? JSONAttemptParse(last.input, {}) : {};
|
||||||
|
} else if(chunk.type === 'message_delta') {
|
||||||
|
if(chunk.usage) usage = chunk.usage;
|
||||||
} else if(chunk.type === 'message_stop') {
|
} else if(chunk.type === 'message_stop') {
|
||||||
break;
|
break;
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
} else {
|
||||||
|
usage = resp.usage;
|
||||||
}
|
}
|
||||||
|
duration = Date.now() - callStart;
|
||||||
|
tps = usage?.output_tokens && duration > 0 ? usage.output_tokens / (duration / 1000) : 0;
|
||||||
|
|
||||||
// Run tools
|
|
||||||
const toolCalls = resp.content.filter((c: any) => c.type === 'tool_use');
|
const toolCalls = resp.content.filter((c: any) => c.type === 'tool_use');
|
||||||
if(toolCalls.length && !controller.signal.aborted) {
|
if(toolCalls.length && !controller.signal.aborted) {
|
||||||
history.push({role: 'assistant', content: resp.content});
|
history.push({role: 'assistant', content: resp.content, timestamp: Date.now(), duration, tps});
|
||||||
const results = await Promise.all(toolCalls.map(async (toolCall: any) => {
|
const results = await Promise.all(toolCalls.map(async (toolCall: any) => {
|
||||||
const tool = tools.find(findByProp('name', toolCall.name));
|
const tool = tools.find(findByProp('name', toolCall.name));
|
||||||
if(options.stream) options.stream({tool: toolCall.name});
|
if(options.stream) options.stream({tool: toolCall.name});
|
||||||
if(!tool) return {tool_use_id: toolCall.id, is_error: true, content: 'Tool not found'};
|
if(!tool) return {tool_use_id: toolCall.id, is_error: true, content: 'Tool not found'};
|
||||||
try {
|
try {
|
||||||
const result = await tool.fn(toolCall.input, options?.stream, this.ai);
|
const toolStream = options.stream && ((chunk: any) => {
|
||||||
|
if(chunk.done) { terminal = true; return; }
|
||||||
|
options.stream!(chunk);
|
||||||
|
});
|
||||||
|
const result = await tool.fn(toolCall.input, toolStream, this.ai, toolCall.id);
|
||||||
return {type: 'tool_result', tool_use_id: toolCall.id, content: typeof result == 'object' ? JSONSanitize(result) : result};
|
return {type: 'tool_result', tool_use_id: toolCall.id, content: typeof result == 'object' ? JSONSanitize(result) : result};
|
||||||
} catch (err: any) {
|
} catch (err: any) {
|
||||||
return {type: 'tool_result', tool_use_id: toolCall.id, is_error: true, content: err?.message || err?.toString() || 'Unknown'};
|
return {type: 'tool_result', tool_use_id: toolCall.id, is_error: true, content: err?.message || err?.toString() || 'Unknown'};
|
||||||
}
|
}
|
||||||
}));
|
}));
|
||||||
history.push({role: 'user', content: results});
|
history.push({role: 'user', content: results, timestamp: Date.now()});
|
||||||
requestParams.messages = history;
|
requestParams.messages = history;
|
||||||
}
|
}
|
||||||
} while (!controller.signal.aborted && resp.content.some((c: any) => c.type === 'tool_use'));
|
} while (!terminal && !controller.signal.aborted && resp.content.some((c: any) => c.type === 'tool_use'));
|
||||||
|
|
||||||
|
if(!terminal) {
|
||||||
const textContent = resp.content.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
|
const textContent = resp.content.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
|
||||||
history.push({role: 'assistant', content: textContent});
|
history.push({role: 'assistant', content: textContent.trim(), timestamp: Date.now(), duration, tps});
|
||||||
|
}
|
||||||
|
|
||||||
history = this.toStandard(history);
|
history = this.toStandard(history);
|
||||||
|
|
||||||
if(options.stream) options.stream({done: true});
|
|
||||||
if(options.history) options.history.splice(0, options.history.length, ...history);
|
if(options.history) options.history.splice(0, options.history.length, ...history);
|
||||||
|
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) => {
|
||||||
|
if(h.role === 'assistant') return str + (h.content || '');
|
||||||
|
return str;
|
||||||
|
}, '').trim();
|
||||||
|
|
||||||
// Return parsed JSON if schema provided
|
|
||||||
const finalContent = history.at(-1)?.content;
|
|
||||||
res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
|
res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
|
||||||
}), {abort: () => controller.abort()});
|
}), {abort: () => controller.abort()});
|
||||||
}
|
}
|
||||||
|
|||||||
334
src/kd-tree.ts
Normal file
334
src/kd-tree.ts
Normal file
@@ -0,0 +1,334 @@
|
|||||||
|
export type DistanceMetric = "euclidean" | "cosine";
|
||||||
|
|
||||||
|
export interface KDPoint<T = unknown> {
|
||||||
|
vector: number[];
|
||||||
|
payload: T;
|
||||||
|
}
|
||||||
|
|
||||||
|
export interface KNNResult<T = unknown> {
|
||||||
|
point: KDPoint<T>;
|
||||||
|
distance: number;
|
||||||
|
}
|
||||||
|
|
||||||
|
interface KDNode<T> {
|
||||||
|
point: KDPoint<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
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Keeps the k closest candidates in memory, evicts the furthest when full
|
||||||
|
*/
|
||||||
|
class BoundedMaxHeap<T> {
|
||||||
|
private heap: KNNResult<T>[] = [];
|
||||||
|
|
||||||
|
constructor(private readonly k: number) {}
|
||||||
|
|
||||||
|
get size(): number { return this.heap.length; }
|
||||||
|
|
||||||
|
get worstDistance(): number {
|
||||||
|
return this.heap.length < this.k ? Infinity : this.heap[0].distance;
|
||||||
|
}
|
||||||
|
|
||||||
|
push(item: KNNResult<T>): void {
|
||||||
|
if (this.heap.length < this.k) {
|
||||||
|
this.heap.push(item);
|
||||||
|
this.bubbleUp(this.heap.length - 1);
|
||||||
|
} else if (item.distance < this.heap[0].distance) {
|
||||||
|
this.heap[0] = item;
|
||||||
|
this.sinkDown(0);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
toSortedArray(): KNNResult<T>[] {
|
||||||
|
return [...this.heap].sort((a, b) => a.distance - b.distance);
|
||||||
|
}
|
||||||
|
|
||||||
|
private bubbleUp(i: number): void {
|
||||||
|
while (i > 0) {
|
||||||
|
const parent = (i - 1) >> 1;
|
||||||
|
if (this.heap[parent].distance >= this.heap[i].distance) break;
|
||||||
|
[this.heap[parent], this.heap[i]] = [this.heap[i], this.heap[parent]];
|
||||||
|
i = parent;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
private sinkDown(i: number): void {
|
||||||
|
const n = this.heap.length;
|
||||||
|
while (true) {
|
||||||
|
let largest = i;
|
||||||
|
const l = 2 * i + 1, r = 2 * i + 2;
|
||||||
|
if (l < n && this.heap[l].distance > this.heap[largest].distance) largest = l;
|
||||||
|
if (r < n && this.heap[r].distance > this.heap[largest].distance) largest = r;
|
||||||
|
if (largest === i) break;
|
||||||
|
[this.heap[largest], this.heap[i]] = [this.heap[i], this.heap[largest]];
|
||||||
|
i = largest;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* K-D Tree for efficient nearest-neighbor search over high-dimensional vectors / embeddings.
|
||||||
|
*
|
||||||
|
* Supports:
|
||||||
|
* - Insertion of labeled points
|
||||||
|
* - k-nearest-neighbor (KNN) search
|
||||||
|
* - Radius search (all points within a given distance)
|
||||||
|
* - Euclidean and cosine distance metrics
|
||||||
|
* - Bulk construction (balanced tree) for best query performance
|
||||||
|
*/
|
||||||
|
export class KDTree<T = unknown> {
|
||||||
|
private root: KDNode<T> | null = null;
|
||||||
|
private _size = 0;
|
||||||
|
private readonly dims: number;
|
||||||
|
private readonly distanceFn: (a: number[], b: number[]) => number;
|
||||||
|
|
||||||
|
/**
|
||||||
|
* @param dims Dimensionality of all vectors (must be consistent).
|
||||||
|
* @param metric Distance metric to use. Default: "euclidean".
|
||||||
|
* @param points Optional initial set of points. Builds a balanced tree
|
||||||
|
* in O(n log² n) — prefer this over inserting one-by-one
|
||||||
|
* when you have a large corpus.
|
||||||
|
*/
|
||||||
|
constructor(
|
||||||
|
dims: number,
|
||||||
|
metric: DistanceMetric = "euclidean",
|
||||||
|
points?: KDPoint<T>[]
|
||||||
|
) {
|
||||||
|
this.dims = dims;
|
||||||
|
this.distanceFn = metric === "cosine" ? cosine : euclidean;
|
||||||
|
|
||||||
|
if (points && points.length > 0) {
|
||||||
|
this.validateAll(points);
|
||||||
|
this.root = this.buildBalanced([...points], 0);
|
||||||
|
this._size = points.length;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
/** Total number of points stored in the tree. */
|
||||||
|
get size(): number { return this._size; }
|
||||||
|
|
||||||
|
// ── Insertion ──────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Insert a single point. O(log n) average, O(n) worst case on skewed data.
|
||||||
|
* For bulk loading prefer passing points to the constructor.
|
||||||
|
*/
|
||||||
|
insert(point: KDPoint<T>): void {
|
||||||
|
this.validate(point);
|
||||||
|
this.root = this.insertNode(this.root, point, 0);
|
||||||
|
this._size++;
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── KNN search ─────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Find the k nearest neighbors to `query`.
|
||||||
|
* Returns results sorted by distance ascending.
|
||||||
|
*/
|
||||||
|
knn(query: number[], k: number): KNNResult<T>[] {
|
||||||
|
if (k <= 0) throw new RangeError("k must be a positive integer");
|
||||||
|
this.validateVector(query);
|
||||||
|
|
||||||
|
const heap = new BoundedMaxHeap<T>(k);
|
||||||
|
this.searchKNN(this.root, query, k, heap, 0);
|
||||||
|
return heap.toSortedArray();
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Nearest single neighbor. Convenience wrapper around knn(query, 1).
|
||||||
|
* Returns null if the tree is empty.
|
||||||
|
*/
|
||||||
|
nearest(query: number[]): KNNResult<T> | null {
|
||||||
|
const results = this.knn(query, 1);
|
||||||
|
return results[0] ?? null;
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── Radius search ──────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Return all points whose distance to `query` is ≤ `radius`,
|
||||||
|
* sorted by distance ascending.
|
||||||
|
*/
|
||||||
|
radiusSearch(query: number[], radius: number): KNNResult<T>[] {
|
||||||
|
if (radius < 0) throw new RangeError("radius must be non-negative");
|
||||||
|
this.validateVector(query);
|
||||||
|
|
||||||
|
const results: KNNResult<T>[] = [];
|
||||||
|
this.searchRadius(this.root, query, radius, results, 0);
|
||||||
|
results.sort((a, b) => a.distance - b.distance);
|
||||||
|
return results;
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── Conversion ─────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
/** Collect all points in the tree (order not guaranteed). */
|
||||||
|
toArray(): KDPoint<T>[] {
|
||||||
|
const out: KDPoint<T>[] = [];
|
||||||
|
this.collect(this.root, out);
|
||||||
|
return out;
|
||||||
|
}
|
||||||
|
|
||||||
|
/**
|
||||||
|
* Rebuild the tree from its current points as a balanced tree.
|
||||||
|
* Useful after many individual insertions to restore O(log n) query time.
|
||||||
|
*/
|
||||||
|
rebalance(): void {
|
||||||
|
const points = this.toArray();
|
||||||
|
this.root = points.length ? this.buildBalanced(points, 0) : null;
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── Private: build ─────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
private buildBalanced(points: KDPoint<T>[], depth: number): KDNode<T> {
|
||||||
|
const axis = depth % this.dims;
|
||||||
|
points.sort((a, b) => a.vector[axis] - b.vector[axis]);
|
||||||
|
|
||||||
|
const mid = Math.floor(points.length / 2);
|
||||||
|
return {
|
||||||
|
point: points[mid],
|
||||||
|
axis,
|
||||||
|
left: points.slice(0, mid).length
|
||||||
|
? this.buildBalanced(points.slice(0, mid), depth + 1)
|
||||||
|
: null,
|
||||||
|
right: points.slice(mid + 1).length
|
||||||
|
? this.buildBalanced(points.slice(mid + 1), depth + 1)
|
||||||
|
: null,
|
||||||
|
};
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── Private: insert ────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
private insertNode(
|
||||||
|
node: KDNode<T> | null,
|
||||||
|
point: KDPoint<T>,
|
||||||
|
depth: number
|
||||||
|
): KDNode<T> {
|
||||||
|
if (node === null) {
|
||||||
|
return { point, axis: depth % this.dims, left: null, right: null };
|
||||||
|
}
|
||||||
|
const axis = depth % this.dims;
|
||||||
|
if (point.vector[axis] < node.point.vector[axis]) {
|
||||||
|
node.left = this.insertNode(node.left, point, depth + 1);
|
||||||
|
} else {
|
||||||
|
node.right = this.insertNode(node.right, point, depth + 1);
|
||||||
|
}
|
||||||
|
return node;
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── Private: KNN traversal ─────────────────────────────────────────────────
|
||||||
|
|
||||||
|
private searchKNN(
|
||||||
|
node: KDNode<T> | null,
|
||||||
|
query: number[],
|
||||||
|
k: number,
|
||||||
|
heap: BoundedMaxHeap<T>,
|
||||||
|
depth: number
|
||||||
|
): void {
|
||||||
|
if (node === null) return;
|
||||||
|
|
||||||
|
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];
|
||||||
|
const [near, far] = diff <= 0
|
||||||
|
? [node.left, node.right]
|
||||||
|
: [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
|
||||||
|
? true
|
||||||
|
: Math.abs(diff) < heap.worstDistance;
|
||||||
|
|
||||||
|
if (shouldExplore) {
|
||||||
|
this.searchKNN(far, query, k, heap, depth + 1);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── Private: radius traversal ──────────────────────────────────────────────
|
||||||
|
|
||||||
|
private searchRadius(
|
||||||
|
node: KDNode<T> | null,
|
||||||
|
query: number[],
|
||||||
|
radius: number,
|
||||||
|
results: KNNResult<T>[],
|
||||||
|
depth: number
|
||||||
|
): void {
|
||||||
|
if (node === null) return;
|
||||||
|
|
||||||
|
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];
|
||||||
|
const [near, far] = diff <= 0
|
||||||
|
? [node.left, node.right]
|
||||||
|
: [node.right, node.left];
|
||||||
|
|
||||||
|
this.searchRadius(near, query, radius, results, depth + 1);
|
||||||
|
|
||||||
|
const shouldExplore =
|
||||||
|
this.distanceFn === cosine ? true : Math.abs(diff) <= radius;
|
||||||
|
|
||||||
|
if (shouldExplore) {
|
||||||
|
this.searchRadius(far, query, radius, results, depth + 1);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── Private: collect ───────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
private collect(node: KDNode<T> | null, out: KDPoint<T>[]): void {
|
||||||
|
if (node === null) return;
|
||||||
|
out.push(node.point);
|
||||||
|
this.collect(node.left, out);
|
||||||
|
this.collect(node.right, out);
|
||||||
|
}
|
||||||
|
|
||||||
|
// ── Private: validation ────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
private validateVector(v: number[]): void {
|
||||||
|
if (v.length !== this.dims) {
|
||||||
|
throw new TypeError(
|
||||||
|
`Vector length ${v.length} does not match tree dimensionality ${this.dims}`
|
||||||
|
);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
private validate(point: KDPoint<T>): void {
|
||||||
|
this.validateVector(point.vector);
|
||||||
|
}
|
||||||
|
|
||||||
|
private validateAll(points: KDPoint<T>[]): void {
|
||||||
|
for (const p of points) this.validate(p);
|
||||||
|
}
|
||||||
|
}
|
||||||
242
src/llm.ts
242
src/llm.ts
@@ -1,3 +1,4 @@
|
|||||||
|
import {snakeCase} from '@ztimson/utils';
|
||||||
import {AbortablePromise, Ai} from './ai.ts';
|
import {AbortablePromise, Ai} from './ai.ts';
|
||||||
import {Anthropic} from './antrhopic.ts';
|
import {Anthropic} from './antrhopic.ts';
|
||||||
import {OpenAi} from './open-ai.ts';
|
import {OpenAi} from './open-ai.ts';
|
||||||
@@ -6,11 +7,24 @@ import {AiTool, AiToolArg} from './tools.ts';
|
|||||||
import {fileURLToPath} from 'url';
|
import {fileURLToPath} from 'url';
|
||||||
import {dirname, join} from 'path';
|
import {dirname, join} from 'path';
|
||||||
import {spawn} from 'node:child_process';
|
import {spawn} from 'node:child_process';
|
||||||
import {Memory, MemoryManager} from './memory.ts';
|
import {Memory, MemoryCache, MemoryManager, MemoryOptions} from './memory.ts';
|
||||||
|
|
||||||
export type AnthropicConfig = {proto: 'anthropic', token: string};
|
export type AnthropicConfig = {proto: 'anthropic', token: string};
|
||||||
export type OpenAiConfig = {proto: 'openai', host?: string, token: string};
|
export type OpenAiConfig = {proto: 'openai', host?: string, token: string};
|
||||||
|
|
||||||
|
export type Agent = {
|
||||||
|
name: string;
|
||||||
|
description?: string;
|
||||||
|
model?: string | null;
|
||||||
|
temperature?: number;
|
||||||
|
system: string;
|
||||||
|
delegate?: boolean;
|
||||||
|
skills?: Skill[] | null;
|
||||||
|
tools?: AiTool[] | null;
|
||||||
|
mcp?: McpServer[] | null;
|
||||||
|
agents?: string[] | null;
|
||||||
|
}
|
||||||
|
|
||||||
export type LLMMessage = {
|
export type LLMMessage = {
|
||||||
/** Message originator */
|
/** Message originator */
|
||||||
role: 'assistant' | 'system' | 'user';
|
role: 'assistant' | 'system' | 'user';
|
||||||
@@ -33,6 +47,10 @@ export type LLMMessage = {
|
|||||||
error?: undefined | string;
|
error?: undefined | string;
|
||||||
/** Timestamp */
|
/** Timestamp */
|
||||||
timestamp?: number;
|
timestamp?: number;
|
||||||
|
/** Response duration in ms */
|
||||||
|
duration?: number;
|
||||||
|
/** Tokens per second */
|
||||||
|
tps?: number;
|
||||||
}
|
}
|
||||||
|
|
||||||
export type LLMRequest = {
|
export type LLMRequest = {
|
||||||
@@ -55,13 +73,17 @@ export type LLMRequest = {
|
|||||||
/** Compress old messages in the chat to free up context */
|
/** Compress old messages in the chat to free up context */
|
||||||
compress?: {max: number; min: number};
|
compress?: {max: number; min: number};
|
||||||
/** User's memory documents - RAG injected automatically each turn */
|
/** User's memory documents - RAG injected automatically each turn */
|
||||||
memory?: Memory[];
|
memory?: Memory[] | MemoryCache | MemoryOptions;
|
||||||
/** Model to use for memory operations */
|
/** Model to use for memory operations */
|
||||||
memoryModel?: string;
|
memoryModel?: string;
|
||||||
/** Skill documents the AI can browse and read on demand */
|
/** Skill documents the AI can browse and read on demand */
|
||||||
skills?: Skill[];
|
skills?: Skill[];
|
||||||
/** MCP servers to connect and expose as tools */
|
/** MCP servers to connect and expose as tools */
|
||||||
mcp?: McpServer[];
|
mcp?: McpServer[];
|
||||||
|
/** Subagents exposed as delegatable/wrapped tools */
|
||||||
|
agents?: Agent[];
|
||||||
|
/** @internal recursion guard for nested agent delegation */
|
||||||
|
_agentDepth?: number;
|
||||||
}
|
}
|
||||||
|
|
||||||
export type McpServer = {
|
export type McpServer = {
|
||||||
@@ -82,6 +104,7 @@ export type Skill = {
|
|||||||
content: string;
|
content: string;
|
||||||
}
|
}
|
||||||
|
|
||||||
|
const MAX_AGENT_DEPTH = 5;
|
||||||
|
|
||||||
class LLM {
|
class LLM {
|
||||||
private memoryManager!: MemoryManager;
|
private memoryManager!: MemoryManager;
|
||||||
@@ -99,6 +122,59 @@ class LLM {
|
|||||||
this.memoryManager = new MemoryManager(this);
|
this.memoryManager = new MemoryManager(this);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
private setupAgent(agents: Agent[] = [], allAgents: Agent[], pending: Map<string, any>, aborts: (() => void)[], depth = 0): AiTool[] {
|
||||||
|
return agents.map(a => {
|
||||||
|
const toolName = `${a.delegate ? '' : 'sub'}agent_${snakeCase(a.name)}`;
|
||||||
|
return {
|
||||||
|
name: toolName,
|
||||||
|
description: `${a.delegate ? 'Delegate to ' : ''}Subagent: ${a.description || a.name}`,
|
||||||
|
args: {
|
||||||
|
context: {type: 'string', description: 'Summary of related messages, samples, files, etc...', required: true},
|
||||||
|
instructions: {type: 'string', description: 'Detailed instructions for subagent to complete', required: true},
|
||||||
|
},
|
||||||
|
fn: async (args: any, stream: any, ai: any, id?: string) => {
|
||||||
|
if(depth >= MAX_AGENT_DEPTH) return 'Max agent delegation depth exceeded';
|
||||||
|
const subHistory: LLMMessage[] = [];
|
||||||
|
|
||||||
|
// Opt-in only, self always excluded regardless of whitelist
|
||||||
|
const nested = (a.agents || [])
|
||||||
|
.map(name => allAgents.find(x => x.name === name))
|
||||||
|
.filter((x): x is Agent => !!x && x.name !== a.name);
|
||||||
|
|
||||||
|
const start = Date.now();
|
||||||
|
const request = this.ask(`${args.instructions}${args.context ? `\n\n<context>${args.context}</context>` : ''}`, {
|
||||||
|
system: `You are a specialized subagent. ${a.delegate ? 'Your output streams directly to the user for the remainder of this turn.' : 'You are wrapped in a tool call that will be analysis by an LLM'}
|
||||||
|
As a subagent, focus on executing your task completely using available tools and returning only the final result - no commentary, questions, or dialogue.
|
||||||
|
|
||||||
|
${a.system}`,
|
||||||
|
model: a.model || undefined,
|
||||||
|
temperature: a.temperature,
|
||||||
|
stream: a.delegate ? stream : undefined,
|
||||||
|
history: subHistory,
|
||||||
|
mcp: a.mcp || undefined,
|
||||||
|
skills: a.skills || undefined,
|
||||||
|
tools: a.tools || undefined,
|
||||||
|
agents: nested,
|
||||||
|
_agentDepth: depth + 1,
|
||||||
|
} as any);
|
||||||
|
aborts.push(request.abort);
|
||||||
|
const resp = await request;
|
||||||
|
const duration = Date.now() - start;
|
||||||
|
const assistantTurns = subHistory.filter((h: any) => h.role === 'assistant' && h.duration);
|
||||||
|
const genTime = assistantTurns.reduce((s, h: any) => s + h.duration, 0);
|
||||||
|
const genTokens = assistantTurns.reduce((s, h: any) => s + (h.tps || 0) * (h.duration / 1000), 0);
|
||||||
|
const tps = genTime > 0 ? genTokens / (genTime / 1000) : 0;
|
||||||
|
|
||||||
|
if(a.delegate) {
|
||||||
|
pending.set(<string>id, {resp, subHistory, duration, tps});
|
||||||
|
return '';
|
||||||
|
}
|
||||||
|
return resp;
|
||||||
|
}
|
||||||
|
};
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
private async setupMcp(servers: McpServer[] = []): Promise<{prompt: string, tools: AiTool[]}> {
|
private async setupMcp(servers: McpServer[] = []): Promise<{prompt: string, tools: AiTool[]}> {
|
||||||
if(!servers?.length) return {prompt: '', tools: []};
|
if(!servers?.length) return {prompt: '', tools: []};
|
||||||
const allTools: AiTool[] = [];
|
const allTools: AiTool[] = [];
|
||||||
@@ -143,7 +219,7 @@ class LLM {
|
|||||||
return {
|
return {
|
||||||
prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
|
prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
|
||||||
tools: [{
|
tools: [{
|
||||||
name: 'read_skill',
|
name: 'skill_read',
|
||||||
description: 'Read the full content of a skill/knowledge document',
|
description: 'Read the full content of a skill/knowledge document',
|
||||||
args: {
|
args: {
|
||||||
name: {type: 'string', description: 'Exact skill name', required: true}
|
name: {type: 'string', description: 'Exact skill name', required: true}
|
||||||
@@ -157,6 +233,20 @@ class LLM {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
|
private wrapToolTiming(tools: AiTool[], timings: Map<string, {duration: number, tps: number}>): AiTool[] {
|
||||||
|
return tools.map(t => ({
|
||||||
|
...t,
|
||||||
|
fn: async (args: any, stream: any, ai: any, id?: string) => {
|
||||||
|
const start = Date.now();
|
||||||
|
const result = await t.fn(args, stream, ai, id);
|
||||||
|
const duration = Date.now() - start;
|
||||||
|
const tps = duration > 0 ? this.estimateTokens(result) / (duration / 1000) : 0;
|
||||||
|
if(id) timings.set(id, {duration, tps});
|
||||||
|
return result;
|
||||||
|
}
|
||||||
|
}));
|
||||||
|
}
|
||||||
|
|
||||||
ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
|
ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
|
||||||
options = <any>{
|
options = <any>{
|
||||||
system: '',
|
system: '',
|
||||||
@@ -167,8 +257,16 @@ class LLM {
|
|||||||
}
|
}
|
||||||
const m = options.model || this.defaultModel;
|
const m = options.model || this.defaultModel;
|
||||||
if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
|
if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
|
||||||
let abort = () => {};
|
let request: AbortablePromise<string> | null = null;
|
||||||
return Object.assign(new Promise<string>(async res => {
|
let aborted = false;
|
||||||
|
const nestedAborts: (() => void)[] = [];
|
||||||
|
const abort = () => {
|
||||||
|
aborted = true;
|
||||||
|
request?.abort?.();
|
||||||
|
nestedAborts.forEach(a => a());
|
||||||
|
};
|
||||||
|
|
||||||
|
const promise = (async () => {
|
||||||
let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
|
let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
|
||||||
const prompts: string[] = [];
|
const prompts: string[] = [];
|
||||||
let history = options.history || [];
|
let history = options.history || [];
|
||||||
@@ -189,47 +287,105 @@ class LLM {
|
|||||||
tools.push(...s.tools);
|
tools.push(...s.tools);
|
||||||
}
|
}
|
||||||
|
|
||||||
|
// Agents
|
||||||
|
const agents = options.agents || this.ai.options?.llm?.agents;
|
||||||
|
const pendingDelegates = new Map<string, any>();
|
||||||
|
if(agents?.length) tools.push(...this.setupAgent(agents, agents, pendingDelegates, nestedAborts, options._agentDepth || 0));
|
||||||
|
|
||||||
// Memory
|
// Memory
|
||||||
if(options.memory) {
|
const mem = MemoryManager.normalize(options.memory);
|
||||||
const relevant = await this.memoryManager.recollect(message, options.memory, 1);
|
if(mem) {
|
||||||
prompts.unshift(`You have access to the following memory files:
|
const mems = mem.memory instanceof MemoryCache ? mem.memory.memories : mem.memory;
|
||||||
${options.memory.map(m => `- ${m.name}: ${m.description}`).join('\n')}
|
if(mems.length) {
|
||||||
${relevant.length ? `
|
if(mem.inject) {
|
||||||
The closest memory has been added primitively:
|
const pool = 15; // candidates considered, cheap since only refs are listed
|
||||||
\`\`\`
|
const budget = mem.maxTokens ?? 2000; // actual content injected
|
||||||
Name: ${relevant[0].name}
|
const relevant = await this.memoryManager.recollect(message, mem.memory, pool);
|
||||||
Description: ${relevant[0].description}
|
|
||||||
${relevant[0].content}
|
let used = 0;
|
||||||
\`\`\`
|
const preloaded: typeof relevant = [];
|
||||||
`: ''}`.trim());
|
const listed: typeof relevant = [];
|
||||||
tools.push(this.memoryManager.tools.read(<Memory[]>options.memory));
|
for(const r of relevant) {
|
||||||
|
const t = this.estimateTokens(r.content);
|
||||||
|
if(used + t <= budget || preloaded.length === 0) {
|
||||||
|
preloaded.push(r);
|
||||||
|
used += t;
|
||||||
|
} 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());
|
||||||
|
}
|
||||||
|
if(mem.tool) tools.push(this.memoryManager.tools.read(mem.memory));
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
|
||||||
|
|
||||||
|
// Time each tool call's real execution so its history entry gets its own duration/tps
|
||||||
|
const toolTimings = new Map<string, {duration: number, tps: number}>();
|
||||||
|
tools = this.wrapToolTiming(tools, toolTimings);
|
||||||
|
|
||||||
prompts.unshift(options.system || this.ai.options.llm?.system || '');
|
prompts.unshift(options.system || this.ai.options.llm?.system || '');
|
||||||
const resp = await this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
|
request = this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
|
||||||
|
let resp = await request;
|
||||||
|
|
||||||
|
// Providers stamp duration/tps on assistant entries themselves (from real API usage).
|
||||||
|
// Overwrite tool entries with actual tool-execution timing instead of the LLM call timing.
|
||||||
|
for(const h of history) {
|
||||||
|
if(h.role === 'tool' && toolTimings.has(h.id)) Object.assign(h, toolTimings.get(h.id));
|
||||||
|
}
|
||||||
|
|
||||||
|
// Spice delegated agents response into history
|
||||||
|
let lastDelegateResp: string | null = null;
|
||||||
|
if(pendingDelegates.size) {
|
||||||
|
for(let i = 0; i < history.length; i++) {
|
||||||
|
const h: any = history[i];
|
||||||
|
if(h.role !== 'tool' || !pendingDelegates.has(h.id)) continue;
|
||||||
|
const {resp: delegateResp, subHistory, duration, tps} = pendingDelegates.get(h.id)!;
|
||||||
|
pendingDelegates.delete(h.id);
|
||||||
|
const insert: LLMMessage[] = [...subHistory.filter(sh => sh.role === 'tool'), {role: 'assistant', content: delegateResp, timestamp: Date.now(), duration, tps}];
|
||||||
|
history.splice(i + 1, 0, ...insert);
|
||||||
|
lastDelegateResp = delegateResp;
|
||||||
|
i += insert.length;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// If the orchestrator added no commentary of its own, its answer IS the delegate's answer
|
||||||
|
if(typeof resp === 'string' && !resp.trim() && lastDelegateResp !== null) resp = lastDelegateResp;
|
||||||
|
|
||||||
// Trim memory injections from history
|
// Trim memory injections from history
|
||||||
if(options.memory) {
|
if(mem?.tool) history.splice(0, history.length, ...history.filter(h => h.role !== 'tool' || h.name !== 'memory_recall'));
|
||||||
history.splice(0, history.length, ...history.filter(h => h.role !== 'tool' || h.name !== 'recall'));
|
|
||||||
}
|
|
||||||
|
|
||||||
// Auto-memorize before compressing
|
// Auto-memorize before compressing
|
||||||
if(options.compress && this.estimateTokens(history) >= options.compress.max) {
|
if(options.compress && this.estimateTokens(history) >= options.compress.max) {
|
||||||
if(options.memory) await this.memoryManager.memorize(history, options.memory, options);
|
if(mem?.update) await this.memoryManager.memorize(history, mem.memory, {model: options.memoryModel || this.defaultModel, ...options});
|
||||||
const compressed = await this.compressHistory(history, options.compress.max, options.compress.min, options);
|
const compressed = await this.compressHistory(history, options.compress.max, options.compress.min, options);
|
||||||
if(options.history) options.history.splice(0, options.history.length, ...compressed);
|
if(options.history) options.history.splice(0, options.history.length, ...compressed);
|
||||||
}
|
}
|
||||||
|
|
||||||
return res(resp);
|
return resp;
|
||||||
}), {abort});
|
})();
|
||||||
|
|
||||||
|
return Object.assign(promise, {abort});
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* Digest full conversation history into memory documents.
|
* Digest full conversation history into memory documents.
|
||||||
* Call on session end to persist the conversation.
|
* Call on session end to persist the conversation.
|
||||||
*/
|
*/
|
||||||
async updateMemory(history: LLMMessage[], memories: Memory[], options: LLMRequest = {}): Promise<void> {
|
async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<Memory[]> {
|
||||||
await this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
|
return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
/**
|
||||||
@@ -382,15 +538,33 @@ ${relevant[0].content}
|
|||||||
* @param {string} searchTerms Multiple search terms to check against target
|
* @param {string} searchTerms Multiple search terms to check against target
|
||||||
* @returns {{avg: number, max: number, similarities: number[]}} Similarity values 0-1: 0 = unique, 1 = identical
|
* @returns {{avg: number, max: number, similarities: number[]}} Similarity values 0-1: 0 = unique, 1 = identical
|
||||||
*/
|
*/
|
||||||
fuzzyMatch(target: string, ...searchTerms: string[]) {
|
fuzzyMatch(target, ...searchTerms) {
|
||||||
if (searchTerms.length < 2) throw new Error('Requires at least 2 strings to compare');
|
if (searchTerms.length < 2) throw new Error('Requires at least 2 strings to compare');
|
||||||
const vector = (text: string, dimensions: number = 10): number[] => {
|
const levenshtein = (a, b) => {
|
||||||
return text.toLowerCase().split('').map((char, index) =>
|
const m = a.length, n = b.length;
|
||||||
(char.charCodeAt(0) * (index + 1)) % dimensions / dimensions).slice(0, dimensions);
|
if (!m) return n;
|
||||||
|
if (!n) return m;
|
||||||
|
const dp = Array.from({length: m + 1}, (_, i) => [i, ...Array(n).fill(0)]);
|
||||||
|
for (let j = 0; j <= n; j++) dp[0][j] = j;
|
||||||
|
for (let i = 1; i <= m; i++) {
|
||||||
|
for (let j = 1; j <= n; j++) {
|
||||||
|
dp[i][j] = a[i - 1] === b[j - 1]
|
||||||
|
? dp[i - 1][j - 1]
|
||||||
|
: 1 + Math.min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]);
|
||||||
}
|
}
|
||||||
const v = vector(target);
|
}
|
||||||
const similarities = searchTerms.map(t => vector(t)).map(refVector => this.cosineSimilarity(v, refVector));
|
return dp[m][n];
|
||||||
return {avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length, max: Math.max(...similarities), similarities};
|
};
|
||||||
|
const similarity = (a, b) => {
|
||||||
|
a = a.toLowerCase(); b = b.toLowerCase();
|
||||||
|
return 1 - levenshtein(a, b) / Math.max(a.length, b.length, 1);
|
||||||
|
};
|
||||||
|
const similarities = searchTerms.map(t => similarity(target, t));
|
||||||
|
return {
|
||||||
|
avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length,
|
||||||
|
max: Math.max(...similarities),
|
||||||
|
similarities
|
||||||
|
};
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
/**
|
||||||
|
|||||||
666
src/memory.ts
666
src/memory.ts
@@ -1,177 +1,575 @@
|
|||||||
// memory.ts
|
|
||||||
import {LLMRequest, LLMMessage} from './llm.ts';
|
import {LLMRequest, LLMMessage} from './llm.ts';
|
||||||
import {AiTool} from './tools.ts';
|
import {AiTool} from './tools.ts';
|
||||||
|
import {KDPoint, KDTree} from './kd-tree.ts';
|
||||||
|
|
||||||
|
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;
|
||||||
|
}
|
||||||
|
|
||||||
/** Background information the AI will be fed as a knowledge document */
|
|
||||||
export type Memory = {
|
export type Memory = {
|
||||||
/** Memory subject */
|
|
||||||
name: string;
|
name: string;
|
||||||
/** Short description of what this document contains - used for RAG retrieval */
|
|
||||||
description: string;
|
description: string;
|
||||||
/** Full markdown content of the document */
|
|
||||||
content: string;
|
content: string;
|
||||||
/** Embedding vector of the description - used for similarity search */
|
|
||||||
embedding: number[];
|
embedding: number[];
|
||||||
}
|
}
|
||||||
|
|
||||||
export type MemoryCollection = {
|
export type MemoryRef = {
|
||||||
/** Memory subject */
|
|
||||||
name: string;
|
name: string;
|
||||||
/** Short description - required if isNew */
|
description: string;
|
||||||
description?: string;
|
}
|
||||||
/** Extracted facts to merge */
|
|
||||||
|
export type FactBucket = {
|
||||||
|
subject: string;
|
||||||
facts: string[];
|
facts: string[];
|
||||||
}
|
}
|
||||||
|
|
||||||
|
export type MemoryNode = {
|
||||||
|
name: string;
|
||||||
|
missing: boolean;
|
||||||
|
links: string[];
|
||||||
|
backlinks: string[];
|
||||||
|
}
|
||||||
|
|
||||||
|
function extractLinks(content: string): string[] {
|
||||||
|
if(!content) return [];
|
||||||
|
const matches = content.matchAll(/\[\[([^\]]+)\]\]/g);
|
||||||
|
return [...new Set([...matches].map(m => m[1].trim()))];
|
||||||
|
}
|
||||||
|
|
||||||
|
export function extractMetadata(content: string): {links: string[], backlinks: string[]} {
|
||||||
|
const match = content.match(/^---\n([\s\S]*?)\n---/);
|
||||||
|
if (!match) return {links: [], backlinks: []};
|
||||||
|
|
||||||
|
const fm = match[1];
|
||||||
|
const getList = (key: string): string[] => {
|
||||||
|
const m = fm.match(new RegExp(`^${key}:\\s*\\[(.*)\\]$`, 'm'));
|
||||||
|
if (!m || !m[1].trim()) return [];
|
||||||
|
return m[1].split(',').map(s => s.trim().replace(/^"|"$/g, '')).filter(Boolean);
|
||||||
|
};
|
||||||
|
|
||||||
|
return {
|
||||||
|
links: getList('links'),
|
||||||
|
backlinks: getList('backlinks'),
|
||||||
|
};
|
||||||
|
}
|
||||||
|
|
||||||
|
function dedupeFacts(facts: string[]): string[] {
|
||||||
|
const seen = new Map<string, string>();
|
||||||
|
for (const f of facts) {
|
||||||
|
const clean = f.trim();
|
||||||
|
if (clean) seen.set(clean.toLowerCase(), clean);
|
||||||
|
}
|
||||||
|
return [...seen.values()];
|
||||||
|
}
|
||||||
|
|
||||||
|
function cosineDistance(a: number[], b: number[]): number {
|
||||||
|
let dot = 0, normA = 0, normB = 0;
|
||||||
|
for (let i = 0; i < a.length; i++) {
|
||||||
|
dot += a[i] * b[i];
|
||||||
|
normA += a[i] * a[i];
|
||||||
|
normB += b[i] * b[i];
|
||||||
|
}
|
||||||
|
const denom = Math.sqrt(normA) * Math.sqrt(normB);
|
||||||
|
return denom === 0 ? 1 : 1 - dot / denom;
|
||||||
|
}
|
||||||
|
|
||||||
|
function getWeekMonday(date: Date = new Date()): string {
|
||||||
|
const d = new Date(Date.UTC(date.getFullYear(), date.getMonth(), date.getDate()));
|
||||||
|
const day = d.getUTCDay();
|
||||||
|
const diff = day === 0 ? -6 : 1 - day;
|
||||||
|
d.setUTCDate(d.getUTCDate() + diff);
|
||||||
|
return d.toISOString().slice(0, 10);
|
||||||
|
}
|
||||||
|
|
||||||
|
function getWeekSunday(monday: string): string {
|
||||||
|
const d = new Date(`${monday}T00:00:00Z`);
|
||||||
|
d.setUTCDate(d.getUTCDate() + 6);
|
||||||
|
return d.toISOString().slice(0, 10);
|
||||||
|
}
|
||||||
|
|
||||||
export class MemoryManager {
|
export class MemoryManager {
|
||||||
|
private recentlyTouched = new Map<string, number>();
|
||||||
|
|
||||||
|
private pendingMemorizations = new Map<string, {
|
||||||
|
memories: Memory[] | MemoryCache,
|
||||||
|
tempMemoryName: string,
|
||||||
|
timestamp: number,
|
||||||
|
}>();
|
||||||
|
|
||||||
|
private queues = new Map<string, {
|
||||||
|
pending: string[],
|
||||||
|
request: {abort?: () => void} | null,
|
||||||
|
task: Promise<void>,
|
||||||
|
}>();
|
||||||
|
|
||||||
tools = {
|
tools = {
|
||||||
edit: (memory: Memory): AiTool => ({
|
read: (memories: Memory[] | MemoryCache): AiTool => ({
|
||||||
name: 'edit_memory',
|
name: 'memory_recall',
|
||||||
description: 'Edit a memory. Omit start/end to append. Pass start only to replace from that line on (Note line 0 = first line of content / line AFTER description). Pass start+end to replace a specific range. start=0 replaces the whole document. Returns updated document',
|
description: 'Read the full content of a memory document',
|
||||||
args: {
|
|
||||||
content: {type: 'string', description: 'New content', required: true},
|
|
||||||
start: {type: 'number', description: 'First line to replace (0-indexed, inclusive). Omit to append.'},
|
|
||||||
end: {type: 'number', description: 'Last line to replace (0-indexed, inclusive). Omit to replace from start to end of doc.'},
|
|
||||||
},
|
|
||||||
fn: (args: any) => {
|
|
||||||
const lines = memory.content ? memory.content.split('\n') : [];
|
|
||||||
const newLines = args.content.split('\n');
|
|
||||||
if(args.start === undefined) lines.push(...newLines);
|
|
||||||
else if(args.end === undefined) lines.splice(args.start, lines.length - args.start, ...newLines);
|
|
||||||
else lines.splice(args.start, args.end - args.start + 1, ...newLines);
|
|
||||||
memory.content = lines.join('\n');
|
|
||||||
return memory.content;
|
|
||||||
}
|
|
||||||
}),
|
|
||||||
extract: (pools: MemoryCollection[]): AiTool => ({
|
|
||||||
name: 'extract_facts',
|
|
||||||
description: 'Extract a list of facts to group into a single memory',
|
|
||||||
args: {
|
|
||||||
name: {type: 'string', description: 'Exact name of an existing memory, or a new name if none fits ([pro]nouns only)', required: true},
|
|
||||||
description: {type: 'string', description: 'One sentence description of the memory subject', required: true},
|
|
||||||
facts: {type: 'string', description: 'Comma separated list of extracted facts', required: true},
|
|
||||||
},
|
|
||||||
fn: (args: any) => {
|
|
||||||
pools.push({
|
|
||||||
name: args.name,
|
|
||||||
description: args.description,
|
|
||||||
facts: args.facts.split(',').map((f: string) => f.trim()).filter(Boolean),
|
|
||||||
});
|
|
||||||
return 'Success';
|
|
||||||
}}),
|
|
||||||
read: (memories: Memory[]): AiTool => ({
|
|
||||||
name: 'read_memory',
|
|
||||||
description: 'Read entire memory',
|
|
||||||
args: {
|
args: {
|
||||||
name: {type: 'string', description: 'Exact memory name', required: true},
|
name: {type: 'string', description: 'Exact memory name', required: true},
|
||||||
},
|
},
|
||||||
fn: (args: any) => {
|
fn: (args: any) => {
|
||||||
const mem = memories.find(m => m.name === args.name);
|
const mems = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
|
const mem = mems.find(m => m.name === args.name);
|
||||||
if (!mem) return 'Document not found';
|
if (!mem) return 'Document not found';
|
||||||
return `Name: ${mem.name}\nDescription: ${mem.description}\n\n${mem.content}`;
|
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: ${args.name}`;
|
||||||
|
},
|
||||||
|
}),
|
||||||
|
};
|
||||||
|
|
||||||
|
constructor(private llm: any) {}
|
||||||
|
|
||||||
|
static normalize(m?: Memory[] | MemoryCache | MemoryOptions) {
|
||||||
|
if(!m) return null;
|
||||||
|
const raw = m instanceof MemoryCache || Array.isArray(m);
|
||||||
|
return raw ? {memory: <Memory[] | MemoryCache>m, inject: true, tool: true, update: true} : {inject: true, tool: true, update: true, ...m};
|
||||||
}
|
}
|
||||||
|
|
||||||
constructor(private llm: any, private model?: string) {}
|
private async createTempMemory(conversation: string): Promise<Memory> {
|
||||||
|
const timestamp = Date.now();
|
||||||
|
const content = `---
|
||||||
|
name: _temp_${timestamp}
|
||||||
|
description: Temporary memory - processing in background
|
||||||
|
tags: [_temporary]
|
||||||
|
links: []
|
||||||
|
backlinks: []
|
||||||
|
modified: ${new Date().toISOString()}
|
||||||
|
---
|
||||||
|
|
||||||
/**
|
# Recent Conversation (Processing)
|
||||||
* Extracts facts from conversation and groups them into individual memories
|
|
||||||
* @param {string} conversation Full conversation formatted as [role]: content
|
|
||||||
* @param {Memory[]} memories The user's memory documents
|
|
||||||
* @param {LLMRequest} options LLM options
|
|
||||||
* @returns {Promise<MemoryCollection[]>} Fact pools grouped by target document
|
|
||||||
*/
|
|
||||||
private async extract(conversation: string, memories: Memory[], options: LLMRequest): Promise<MemoryCollection[]> {
|
|
||||||
const existingDocs = memories.map(m => `Name: ${m.name}\nDescription: ${m.description}`).join('\n\n');
|
|
||||||
const pools: MemoryCollection[] = [];
|
|
||||||
await this.llm.ask(conversation, {
|
|
||||||
model: this.model || options.model,
|
|
||||||
temperature: 0.2,
|
|
||||||
system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long term.
|
|
||||||
Rules:
|
|
||||||
- ONLY extract facts the USER explicitly stated about themselves or their business
|
|
||||||
- ONLY extract decisions that were MADE during this conversation
|
|
||||||
- DO NOT extract anything the AI said, its name, capabilities, or how it introduced itself
|
|
||||||
- DO NOT extract greetings, pleasantries or generic exchanges
|
|
||||||
- If nothing worth remembering was said, dont do anything, skip calling tools
|
|
||||||
|
|
||||||
For each fact decide whether it belongs in an existing document or needs a new one, then call the \`extract_facts\` tool.
|
${conversation}`;
|
||||||
|
const [e] = await this.llm.embedding(content);
|
||||||
|
return {
|
||||||
|
name: `_temp_${timestamp}`,
|
||||||
|
description: 'Temporary memory - processing in background',
|
||||||
|
content,
|
||||||
|
embedding: e?.embedding || [],
|
||||||
|
};
|
||||||
|
}
|
||||||
|
|
||||||
Existing documents:\n${existingDocs || 'None yet.'}`,
|
private applyHeader(content: string, header: string): string {
|
||||||
tools: [this.tools.extract(pools)]
|
return `${header}\n\n${this.stripHeader(content)}`;
|
||||||
|
}
|
||||||
|
|
||||||
|
private async backgroundMemorization(conversation: string, memories: Memory[] | MemoryCache, options: LLMRequest, tempName: string): Promise<void> {
|
||||||
|
const mem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
|
const monday = getWeekMonday();
|
||||||
|
const sunday = getWeekSunday(monday);
|
||||||
|
const buckets = await this.factAgent(conversation, mem, options, monday);
|
||||||
|
if(!buckets.length) return;
|
||||||
|
const jobs = [...buckets].map(({subject, facts}) => {
|
||||||
|
let node = mem.find(m => m.name === subject);
|
||||||
|
if(!node) {
|
||||||
|
node = {name: subject, description: '', content: '', embedding: [],};
|
||||||
|
mem.push(node);
|
||||||
|
}
|
||||||
|
const week = subject.startsWith('Journal/') ? {monday, sunday} : undefined;
|
||||||
|
return this.enqueue(node, facts, mem, options, tempName, week);
|
||||||
});
|
});
|
||||||
return pools;
|
await Promise.all(jobs);
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
private buildHeader(node: Memory, week?: {monday: string, sunday: string}, links: string[] = [], backlinks: string[] = []): string {
|
||||||
* Bot 2 - Editor: merges a pool of facts into a specific document using surgical line-based edits.
|
const tags = node.name.split('/')[0]?.toLowerCase();
|
||||||
* Receives full document content and uses read + amend tools to make precise edits.
|
const lines = [
|
||||||
* @param {MemoryCollection} newMem The fact pool to merge
|
'---',
|
||||||
* @param {Memory[]} memories The user's memory documents
|
`name: ${node.name}`,
|
||||||
* @param {LLMRequest} options LLM options
|
`description: ${node.description || ''}`,
|
||||||
*/
|
tags ? `tags: [${tags}]` : '',
|
||||||
private async edit(newMem: MemoryCollection, memories: Memory[], options: LLMRequest): Promise<void> {
|
links.length ? `links: [${links.map(l => `"${l}"`).join(', ')}]` : 'links: []',
|
||||||
const existing = memories.find(m => m.name === newMem.name);
|
backlinks.length ? `backlinks: [${backlinks.map(l => `"${l}"`).join(', ')}]` : 'backlinks: []',
|
||||||
const mem: Memory = existing || {name: newMem.name, description: newMem.description || '', content: '', embedding: []};
|
week ? `week: ${week.monday} – ${week.sunday}` : '',
|
||||||
const isNew = !existing;
|
`modified: ${new Date().toISOString()}`,
|
||||||
|
'---',
|
||||||
await this.llm.ask(newMem.facts.map(f => `- ${f}`).join('\n'),
|
].filter(Boolean);
|
||||||
{
|
return lines.join('\n');
|
||||||
model: this.model || options.model,
|
|
||||||
temperature: 0.2,
|
|
||||||
system: `You are a document editor. Merge the users list of facts into the following document using the \`edit_memory\` tool; call it as many times as necessary:
|
|
||||||
\`\`\`
|
|
||||||
${mem.content}
|
|
||||||
\`\`\``,
|
|
||||||
tools: [this.tools.edit(mem)]
|
|
||||||
}
|
|
||||||
);
|
|
||||||
|
|
||||||
if(isNew || mem.description !== existing?.description) {
|
|
||||||
const e = await this.llm.embedding(mem.description);
|
|
||||||
mem.embedding = e?.[0]?.embedding;
|
|
||||||
}
|
}
|
||||||
|
|
||||||
if(isNew) memories.push(mem);
|
private cosineSearch(query: number[], memories: Memory[], limit: number): MemoryRef[] {
|
||||||
else {
|
const scored = memories
|
||||||
const idx = memories.findIndex(m => m.name === newMem.name);
|
|
||||||
if(idx >= 0) memories[idx] = mem;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
/**
|
|
||||||
* Find relevant memory documents for a query using description embeddings
|
|
||||||
* @param {string} query The query to search against
|
|
||||||
* @param {Memory[]} memories The user's memory documents
|
|
||||||
* @param {number} limit Max number of results to return
|
|
||||||
* @returns {Promise<Memory[]>} The most relevant memory documents
|
|
||||||
*/
|
|
||||||
async recollect(query: string, memories: Memory[], limit = 5): Promise<Memory[]> {
|
|
||||||
const [e] = await this.llm.embedding(query);
|
|
||||||
return memories
|
|
||||||
.filter(m => m.embedding?.length)
|
.filter(m => m.embedding?.length)
|
||||||
.map(m => ({...m, score: this.llm.cosineSimilarity(m.embedding, e.embedding)}))
|
.map(m => ({
|
||||||
.toSorted((a: any, b: any) => b.score - a.score)
|
ref: {name: m.name, description: m.description},
|
||||||
|
distance: cosineDistance(query, m.embedding),
|
||||||
|
}))
|
||||||
|
.sort((a, b) => a.distance - b.distance)
|
||||||
.slice(0, limit);
|
.slice(0, limit);
|
||||||
|
return scored.map(s => s.ref);
|
||||||
}
|
}
|
||||||
|
|
||||||
/**
|
/**
|
||||||
* Two-stage memory pipeline: classify facts from conversation history then surgically merge them into documents.
|
* Coalescing queue: if a doc is already compiling, abort the in-flight run, merge its
|
||||||
* Bot 1 (classify) extracts and groups facts cheaply. Bot 2 (edit) runs per-document in parallel with full content access.
|
* facts with the new ones and restart. Never blocks a pending update, never drops facts.
|
||||||
* @param {LLMMessage[]} history Full conversation history to digest
|
|
||||||
* @param {Memory[]} memories The user's memory documents — mutated in place
|
|
||||||
* @param {LLMRequest} options LLM options
|
|
||||||
*/
|
*/
|
||||||
async memorize(history: LLMMessage[], memories: Memory[], options: LLMRequest): Promise<void> {
|
private enqueue(node: Memory, facts: string[], memories: Memory[] | MemoryCache, options: LLMRequest, tempName: string, week?: {monday: string, sunday: string}): Promise<void> {
|
||||||
|
const key = node.name;
|
||||||
|
const existing = this.queues.get(key);
|
||||||
|
if (existing) {
|
||||||
|
existing.pending.push(...facts);
|
||||||
|
existing.request?.abort?.();
|
||||||
|
return existing.task;
|
||||||
|
}
|
||||||
|
|
||||||
|
const entry: {pending: string[], request: {abort?: () => void} | null, task: Promise<void>} = {pending: [...facts], request: null, task: Promise.resolve()};
|
||||||
|
this.queues.set(key, entry);
|
||||||
|
const m = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
|
entry.task = (async () => {
|
||||||
|
while (entry.pending.length) {
|
||||||
|
const batch = dedupeFacts(entry.pending.splice(0, entry.pending.length));
|
||||||
|
const written = await this.docAgent(node, batch, m, options, tempName, week, entry);
|
||||||
|
if (!written) entry.pending.unshift(...batch);
|
||||||
|
}
|
||||||
|
})().finally(() => {
|
||||||
|
this.queues.delete(key);
|
||||||
|
if(!this.queues.size && memories instanceof MemoryCache) memories.rebuild();
|
||||||
|
});
|
||||||
|
return entry.task;
|
||||||
|
}
|
||||||
|
|
||||||
|
private listNodes(memories: Memory[]): MemoryRef[] {
|
||||||
|
return memories.map(m => ({name: m.name, description: m.description}));
|
||||||
|
}
|
||||||
|
|
||||||
|
decay() {
|
||||||
|
for(const [name, ttl] of this.recentlyTouched) {
|
||||||
|
if(ttl <= 1) this.recentlyTouched.delete(name);
|
||||||
|
else this.recentlyTouched.set(name, ttl - 1);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
forget(name: string, memories: Memory[] | MemoryCache): boolean {
|
||||||
|
const mem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
|
const idx = mem.findIndex(m => m.name === name);
|
||||||
|
if (idx === -1) return false;
|
||||||
|
|
||||||
|
for (const node of mem) {
|
||||||
|
const {links, backlinks} = extractMetadata(node.content);
|
||||||
|
const newBacklinks = backlinks.filter(b => b !== name);
|
||||||
|
const newLinks = links.filter(l => l !== name);
|
||||||
|
|
||||||
|
if (newBacklinks.length !== backlinks.length || newLinks.length !== links.length) {
|
||||||
|
node.content = this.updateFrontmatter(node.content, {
|
||||||
|
links: newLinks,
|
||||||
|
backlinks: newBacklinks,
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
mem.splice(idx, 1);
|
||||||
|
|
||||||
|
if (memories instanceof MemoryCache) memories.rebuild();
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
getTouched(): string[] {
|
||||||
|
return [...this.recentlyTouched.keys()];
|
||||||
|
}
|
||||||
|
|
||||||
|
async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest): Promise<Memory[]> {
|
||||||
const conversation = history
|
const conversation = history
|
||||||
.filter(h => h.role === 'user' || h.role === 'assistant')
|
.filter(h => h.role === 'user' || h.role === 'assistant')
|
||||||
.map(h => `[${h.role}]: ${h.content}`)
|
.map(h => `[${h.role}]: ${h.content}`).join('\n\n').trim();
|
||||||
.join('\n\n');
|
if(!conversation) return [];
|
||||||
if(!conversation.trim()) return;
|
|
||||||
const pools = await this.extract(conversation, memories, options);
|
const trackingId = `${Date.now()}_${Math.random()}`;
|
||||||
if(!pools.length) return;
|
const tempMemory = await this.createTempMemory(conversation);
|
||||||
await Promise.all(pools.map(pool => this.edit(pool, memories, options)));
|
const mem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
|
mem.push(tempMemory);
|
||||||
|
if (memories instanceof MemoryCache) memories.rebuild();
|
||||||
|
this.pendingMemorizations.set(trackingId, {
|
||||||
|
memories,
|
||||||
|
tempMemoryName: tempMemory.name,
|
||||||
|
timestamp: Date.now(),
|
||||||
|
});
|
||||||
|
|
||||||
|
try {
|
||||||
|
await this.backgroundMemorization(conversation, memories, options, tempMemory.name);
|
||||||
|
const finalMem = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
|
return finalMem.filter(m => !m.name.startsWith('_temp_'));
|
||||||
|
} finally {
|
||||||
|
const pending = this.pendingMemorizations.get(trackingId);
|
||||||
|
if (pending) {
|
||||||
|
const cleanMem = pending.memories instanceof MemoryCache
|
||||||
|
? pending.memories.memories
|
||||||
|
: pending.memories;
|
||||||
|
const idx = cleanMem.findIndex(m => m.name === pending.tempMemoryName);
|
||||||
|
if (idx !== -1) cleanMem.splice(idx, 1);
|
||||||
|
if (pending.memories instanceof MemoryCache) pending.memories.rebuild();
|
||||||
|
}
|
||||||
|
this.pendingMemorizations.delete(trackingId);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
async recollect(query: string, memories: Memory[] | MemoryCache, limit = 5, graphDepth = 1): Promise<Memory[]> {
|
||||||
|
const mem: Memory[] = memories instanceof MemoryCache ? memories.memories : memories;
|
||||||
|
if (!mem.length) return [];
|
||||||
|
|
||||||
|
const [e] = await this.llm.embedding(query);
|
||||||
|
if (!e) return [];
|
||||||
|
|
||||||
|
let vectorResults: MemoryRef[];
|
||||||
|
if (memories instanceof MemoryCache) vectorResults = memories.search(e.embedding, limit);
|
||||||
|
else vectorResults = this.cosineSearch(e.embedding, mem, limit);
|
||||||
|
const found = new Set<string>(vectorResults.map(r => r.name));
|
||||||
|
|
||||||
|
if (graphDepth > 0) {
|
||||||
|
const frontier = [...found];
|
||||||
|
for (let depth = 0; depth < graphDepth; depth++) {
|
||||||
|
const next: string[] = [];
|
||||||
|
for (const name of frontier) {
|
||||||
|
const node = mem.find(m => m.name === name);
|
||||||
|
if (!node) continue;
|
||||||
|
const {links} = extractMetadata(node.content);
|
||||||
|
for (const link of links) {
|
||||||
|
if (!found.has(link) && mem.find(m => m.name === link)) {
|
||||||
|
found.add(link);
|
||||||
|
next.push(link);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
frontier.splice(0, frontier.length, ...next);
|
||||||
|
if (!frontier.length) break;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const vectorOrder = vectorResults.map(r => r.name);
|
||||||
|
const graphExpansions = [...found].filter(n => !vectorOrder.includes(n));
|
||||||
|
const ordered = [...vectorOrder, ...graphExpansions];
|
||||||
|
return ordered.map(n => mem.find(m => m.name === n)!).filter(Boolean);
|
||||||
|
}
|
||||||
|
|
||||||
|
touch(name: string, ttl = 2) {
|
||||||
|
this.recentlyTouched.set(name, ttl);
|
||||||
|
}
|
||||||
|
|
||||||
|
private updateFrontmatter(content: string, updates: {links?: string[], backlinks?: string[]}): string {
|
||||||
|
const match = content.match(/^---\n([\s\S]*?)\n---\n\n?([\s\S]*)$/);
|
||||||
|
if (!match) return content;
|
||||||
|
|
||||||
|
const [, fm, body] = match;
|
||||||
|
let newFm = fm;
|
||||||
|
|
||||||
|
if (updates.links !== undefined) {
|
||||||
|
const linksList = updates.links.length ? `[${updates.links.map(l => `"${l}"`).join(', ')}]` : '[]';
|
||||||
|
newFm = newFm.replace(/^links:.*$/m, `links: ${linksList}`);
|
||||||
|
}
|
||||||
|
|
||||||
|
if (updates.backlinks !== undefined) {
|
||||||
|
const backlinksList = updates.backlinks.length ? `[${updates.backlinks.map(l => `"${l}"`).join(', ')}]` : '[]';
|
||||||
|
newFm = newFm.replace(/^backlinks:.*$/m, `backlinks: ${backlinksList}`);
|
||||||
|
}
|
||||||
|
|
||||||
|
newFm = newFm.replace(/^modified:.*$/m, `modified: ${new Date().toISOString()}`);
|
||||||
|
|
||||||
|
return `---\n${newFm}\n---\n\n${body}`;
|
||||||
|
}
|
||||||
|
|
||||||
|
private stripHeader(content: string): string {
|
||||||
|
return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
|
||||||
|
}
|
||||||
|
|
||||||
|
private async docAgent(node: Memory, facts: string[], memories: Memory[], options: LLMRequest, tempName: string, week: {monday: string, sunday: string} | undefined, entry: {request: {abort?: () => void} | null}): Promise<boolean> {
|
||||||
|
const {links: oldLinks} = extractMetadata(node.content);
|
||||||
|
const currentBody = this.stripHeader(node.content);
|
||||||
|
let update;
|
||||||
|
try {
|
||||||
|
for(let i = 0; i < 3 && !update?.content; i++) {
|
||||||
|
const request = this.llm.ask(`New Facts:\n${facts.map(f => `- ${f}`).join('\n')}`, {
|
||||||
|
model: options.model,
|
||||||
|
temperature: 0.3,
|
||||||
|
schema: {
|
||||||
|
description: {type: 'string', description: 'One-line description of what this document covers, no formatting or emojis', required: true},
|
||||||
|
content: {type: 'string', description: 'Rewritten document in markdown, without the frontmatter block', required: true},
|
||||||
|
},
|
||||||
|
system: `You are a knowledge base editor. Rewrite the current document below so it incorporates the new facts.
|
||||||
|
|
||||||
|
Formatting rules:
|
||||||
|
- Use Obsidian-style markdown: # headings, **bold** to add emphasis, __italics__ for titles, terms, etc, bullet & numbered lists for grouped 1D data and tables for 2D data
|
||||||
|
- Link related concepts with [[WikiLink]] notation using full paths like [[People/Sarah]] or [[Projects/Website]]
|
||||||
|
- Create links for specific entities (person, place, project, program) and abstract concepts (quantum mechanics, entropy) but skip generics (car, red, dog)
|
||||||
|
- Keep the document concise, factual, and human-readable
|
||||||
|
- Resolve contradictions: the new facts always win — delete the outdated statement entirely, never keep both
|
||||||
|
- Later facts in the list override earlier ones
|
||||||
|
- Do not add frontmatter blocks, filler, preamble, or AI commentary
|
||||||
|
${week ? '- This is a weekly journal entry.\n' : ''}
|
||||||
|
All nodes:
|
||||||
|
${this.listNodes(memories).map(n => n.name).join(', ') || 'none'}
|
||||||
|
|
||||||
|
Current document:
|
||||||
|
\`\`\`markdown
|
||||||
|
${currentBody}
|
||||||
|
\`\`\``}
|
||||||
|
);
|
||||||
|
entry.request = request;
|
||||||
|
update = await request;
|
||||||
|
}
|
||||||
|
} catch (err: any) {
|
||||||
|
if (err?.name === 'AbortError') return false;
|
||||||
|
throw err;
|
||||||
|
} finally {
|
||||||
|
entry.request = null;
|
||||||
|
}
|
||||||
|
|
||||||
|
if(!update?.content) return false;
|
||||||
|
const newLinks = extractLinks(update.content).filter(l => l !== node.name && l !== tempName);
|
||||||
|
const newLinkSet = new Set(newLinks);
|
||||||
|
const oldLinkSet = new Set(oldLinks);
|
||||||
|
|
||||||
|
for (const added of newLinkSet) {
|
||||||
|
if (!oldLinkSet.has(added)) {
|
||||||
|
const target = memories.find(m => m.name === added);
|
||||||
|
if (target) {
|
||||||
|
const {backlinks} = extractMetadata(target.content);
|
||||||
|
if (!backlinks.includes(node.name)) {
|
||||||
|
target.content = this.updateFrontmatter(target.content, {
|
||||||
|
backlinks: [...backlinks, node.name],
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
for (const removed of oldLinkSet) {
|
||||||
|
if (!newLinkSet.has(removed)) {
|
||||||
|
const target = memories.find(m => m.name === removed);
|
||||||
|
if (target) {
|
||||||
|
const {backlinks} = extractMetadata(target.content);
|
||||||
|
target.content = this.updateFrontmatter(target.content, {
|
||||||
|
backlinks: backlinks.filter(b => b !== node.name),
|
||||||
|
});
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const {backlinks} = extractMetadata(node.content);
|
||||||
|
node.description = node.name !== 'Person/User' ? update.description : 'All information about the current user';
|
||||||
|
node.content = this.applyHeader(update.content, this.buildHeader(node, week, newLinks, backlinks));
|
||||||
|
const [e] = await this.llm.embedding(node.content);
|
||||||
|
if(e) node.embedding = e.embedding;
|
||||||
|
return true;
|
||||||
|
}
|
||||||
|
|
||||||
|
private async factAgent(conversation: string, memories: Memory[], options: LLMRequest, weekKey: string): Promise<FactBucket[]> {
|
||||||
|
const buckets = new Map<string, string[]>();
|
||||||
|
await this.llm.ask(conversation, {
|
||||||
|
model: options.model,
|
||||||
|
temperature: 0.2,
|
||||||
|
system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long-term.
|
||||||
|
|
||||||
|
Rules:
|
||||||
|
- ONLY extract current facts the USER explicitly stated about themselves, their work, or their projects
|
||||||
|
- ONLY extract decisions that were MADE during this conversation
|
||||||
|
- DO NOT extract anything the AI said, its capabilities, or meta-conversation about the AI
|
||||||
|
- DO NOT extract greetings, pleasantries, or generic exchanges
|
||||||
|
- DO NOT extract deltas or changes in facts; ONLY the end fact
|
||||||
|
- If nothing worth remembering was said, do not call any tools
|
||||||
|
|
||||||
|
When extracting facts, you MUST also decide the exact destination path:
|
||||||
|
- Use an existing node name if the facts clearly belong there
|
||||||
|
- All information primary about the user should go under "People/User"
|
||||||
|
- When required, create a new path following collection/subject format (e.g., People/Sarah, Projects/Oxide)
|
||||||
|
- For journal entries, use "Journal"
|
||||||
|
|
||||||
|
Available nodes:
|
||||||
|
- Journal
|
||||||
|
${this.listNodes(memories).filter(n => !n.name.includes('_temp_') && !n.name.includes('Journal')).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}`,
|
||||||
|
tools: [{
|
||||||
|
name: 'facts_extract',
|
||||||
|
description: 'Submit facts with their destination',
|
||||||
|
args: {
|
||||||
|
destination: {type: 'string', description: 'Exact existing node name OR new path (e.g. "People/Sarah", "Projects/Oxide")', required: true},
|
||||||
|
facts: {type: 'string', description: 'Comma-separated facts', required: true},
|
||||||
|
},
|
||||||
|
fn: (args: any) => {
|
||||||
|
const subject = args.destination.trim().toLowerCase() === 'journal'
|
||||||
|
? `Journal/${weekKey}` : args.destination.trim();
|
||||||
|
const facts = buckets.get(subject) ?? [];
|
||||||
|
facts.push(...dedupeFacts(String(args.facts).split(',')));
|
||||||
|
buckets.set(subject, facts);
|
||||||
|
return 'Recorded';
|
||||||
|
},
|
||||||
|
}],
|
||||||
|
});
|
||||||
|
return buckets.entries().toArray().map(([subject, facts]) => ({subject, facts}));
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -20,20 +20,24 @@ export class OpenAi extends LLMProvider {
|
|||||||
for(let i = 0; i < history.length; i++) {
|
for(let i = 0; i < history.length; i++) {
|
||||||
const h = history[i];
|
const h = history[i];
|
||||||
if(h.role === 'assistant' && h.tool_calls) {
|
if(h.role === 'assistant' && h.tool_calls) {
|
||||||
const tools = h.tool_calls.map((tc: any) => ({
|
const items: any[] = [];
|
||||||
|
if(h.content) items.push({role: 'assistant', content: h.content, timestamp: h.timestamp, duration: h.duration, tps: h.tps});
|
||||||
|
items.push(...h.tool_calls.map((tc: any) => ({
|
||||||
role: 'tool',
|
role: 'tool',
|
||||||
id: tc.id,
|
id: tc.id,
|
||||||
name: tc.function.name,
|
name: tc.function.name,
|
||||||
args: JSONAttemptParse(tc.function.arguments, {}),
|
args: JSONAttemptParse(tc.function.arguments, {}),
|
||||||
timestamp: h.timestamp
|
timestamp: h.timestamp,
|
||||||
}));
|
duration: h.duration,
|
||||||
history.splice(i, 1, ...tools);
|
tps: h.tps
|
||||||
i += tools.length - 1;
|
})));
|
||||||
} else if(h.role === 'tool' && h.content) {
|
history.splice(i, 1, ...items);
|
||||||
|
i += items.length - 1;
|
||||||
|
} else if(h.role === 'tool') {
|
||||||
const record = history.find(h2 => h.tool_call_id == h2.id);
|
const record = history.find(h2 => h.tool_call_id == h2.id);
|
||||||
if(record) {
|
if(record) {
|
||||||
if(h.content.includes('"error":')) record.error = h.content;
|
if(h.content?.includes('"error":')) record.error = h.content;
|
||||||
else record.content = h.content;
|
else record.content = h.content || '';
|
||||||
}
|
}
|
||||||
history.splice(i, 1);
|
history.splice(i, 1);
|
||||||
i--;
|
i--;
|
||||||
@@ -51,15 +55,16 @@ export class OpenAi extends LLMProvider {
|
|||||||
content: null,
|
content: null,
|
||||||
tool_calls: [{ id: h.id, type: 'function', function: { name: h.name, arguments: JSON.stringify(h.args) } }],
|
tool_calls: [{ id: h.id, type: 'function', function: { name: h.name, arguments: JSON.stringify(h.args) } }],
|
||||||
refusal: null,
|
refusal: null,
|
||||||
annotations: []
|
annotations: [],
|
||||||
|
timestamp: h.timestamp,
|
||||||
}, {
|
}, {
|
||||||
role: 'tool',
|
role: 'tool',
|
||||||
tool_call_id: h.id,
|
tool_call_id: h.id,
|
||||||
content: h.error || h.content
|
content: h.error || h.content,
|
||||||
|
timestamp: h.timestamp,
|
||||||
});
|
});
|
||||||
} else {
|
} else {
|
||||||
const {timestamp, ...rest} = h;
|
result.push(h);
|
||||||
result.push(rest);
|
|
||||||
}
|
}
|
||||||
return result;
|
return result;
|
||||||
}, [] as any[]);
|
}, [] as any[]);
|
||||||
@@ -68,11 +73,12 @@ export class OpenAi extends LLMProvider {
|
|||||||
ask(message: string, options: LLMRequest = {}): AbortablePromise<string | any> {
|
ask(message: string, options: LLMRequest = {}): AbortablePromise<string | any> {
|
||||||
const controller = new AbortController();
|
const controller = new AbortController();
|
||||||
return Object.assign(new Promise<any>(async (res, rej) => {
|
return Object.assign(new Promise<any>(async (res, rej) => {
|
||||||
if(options.system) {
|
const base = (options.history || []).filter(h => h.role !== 'system');
|
||||||
if(options.history?.[0]?.role != 'system') options.history?.splice(0, 0, {role: 'system', content: options.system, timestamp: Date.now()});
|
let history = this.fromStandard([
|
||||||
else options.history[0].content = options.system;
|
...(options.system ? [{role: <any>'system', content: options.system, timestamp: Date.now()}] : []),
|
||||||
}
|
...base,
|
||||||
let history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
|
{role: 'user', content: message, timestamp: Date.now()}
|
||||||
|
]);
|
||||||
const tools = options.tools || this.ai.options.llm?.tools || [];
|
const tools = options.tools || this.ai.options.llm?.tools || [];
|
||||||
const requestParams: any = {
|
const requestParams: any = {
|
||||||
model: options.model || this.model,
|
model: options.model || this.model,
|
||||||
@@ -106,25 +112,27 @@ export class OpenAi extends LLMProvider {
|
|||||||
};
|
};
|
||||||
}
|
}
|
||||||
|
|
||||||
let resp: any, isFirstMessage = true;
|
if(options.stream) requestParams.stream_options = {include_usage: true};
|
||||||
|
let resp: any, terminal = false, duration = 0, tps = 0;
|
||||||
do {
|
do {
|
||||||
|
requestParams.messages = history.map(({timestamp, ...m}) => m);
|
||||||
|
const callStart = Date.now();
|
||||||
resp = await this.client.chat.completions.create(requestParams).catch(err => {
|
resp = await this.client.chat.completions.create(requestParams).catch(err => {
|
||||||
err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
|
err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
|
||||||
throw err;
|
throw err;
|
||||||
});
|
});
|
||||||
|
|
||||||
|
let usage: any;
|
||||||
if(options.stream) {
|
if(options.stream) {
|
||||||
if(!isFirstMessage) options.stream({text: '\n\n'});
|
resp.choices = [{message: {role: 'assistant', content: '', tool_calls: [], timestamp: Date.now()}}];
|
||||||
else isFirstMessage = false;
|
|
||||||
resp.choices = [{message: {role: 'assistant', content: '', tool_calls: []}}];
|
|
||||||
for await (const chunk of resp) {
|
for await (const chunk of resp) {
|
||||||
if(controller.signal.aborted) break;
|
if(controller.signal.aborted) break;
|
||||||
if(chunk.choices[0].delta.content) {
|
if(chunk.usage) usage = chunk.usage;
|
||||||
|
if(chunk.choices[0]?.delta?.content) {
|
||||||
resp.choices[0].message.content += chunk.choices[0].delta.content;
|
resp.choices[0].message.content += chunk.choices[0].delta.content;
|
||||||
options.stream({text: chunk.choices[0].delta.content});
|
options.stream({text: chunk.choices[0].delta.content});
|
||||||
}
|
}
|
||||||
|
if(chunk.choices[0]?.delta?.tool_calls) {
|
||||||
if(chunk.choices[0].delta.tool_calls) {
|
|
||||||
for(const deltaTC of chunk.choices[0].delta.tool_calls) {
|
for(const deltaTC of chunk.choices[0].delta.tool_calls) {
|
||||||
const existing = resp.choices[0].message.tool_calls.find(tc => tc.index === deltaTC.index);
|
const existing = resp.choices[0].message.tool_calls.find(tc => tc.index === deltaTC.index);
|
||||||
if(existing) {
|
if(existing) {
|
||||||
@@ -149,38 +157,52 @@ export class OpenAi extends LLMProvider {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
} else {
|
||||||
|
usage = resp.usage;
|
||||||
}
|
}
|
||||||
|
duration = Date.now() - callStart;
|
||||||
|
tps = usage?.completion_tokens && duration > 0 ? usage.completion_tokens / (duration / 1000) : 0;
|
||||||
|
|
||||||
if(resp.error) throw new Error(resp.error);
|
if(resp.error) throw new Error(resp.error);
|
||||||
const toolCalls = resp.choices[0].message.tool_calls || [];
|
const toolCalls = resp.choices[0].message.tool_calls || [];
|
||||||
if(toolCalls.length && !controller.signal.aborted) {
|
if(toolCalls.length && !controller.signal.aborted) {
|
||||||
history.push(resp.choices[0].message);
|
history.push({...resp.choices[0].message, duration, tps});
|
||||||
const results = await Promise.all(toolCalls.map(async (toolCall: any) => {
|
const results = await Promise.all(toolCalls.map(async (toolCall: any) => {
|
||||||
const tool = tools?.find(findByProp('name', toolCall.function.name));
|
const tool = tools?.find(findByProp('name', toolCall.function.name));
|
||||||
if(options.stream) options.stream({tool: toolCall.function.name});
|
if(options.stream) options.stream({tool: toolCall.function.name});
|
||||||
if(!tool) return {role: 'tool', tool_call_id: toolCall.id, content: '{"error": "Tool not found"}'};
|
if(!tool) return {role: 'tool', tool_call_id: toolCall.id, content: '{"error": "Tool not found"}', timestamp: Date.now()};
|
||||||
try {
|
try {
|
||||||
const args = JSONAttemptParse(toolCall.function.arguments, {});
|
const args = JSONAttemptParse(toolCall.function.arguments, {});
|
||||||
const result = await tool.fn(args, options.stream, this.ai);
|
const toolStream = options.stream && ((chunk: any) => {
|
||||||
return {role: 'tool', tool_call_id: toolCall.id, content: typeof result == 'object' ? JSONSanitize(result) : result};
|
if(chunk.done) { terminal = true; return; }
|
||||||
|
options.stream!(chunk);
|
||||||
|
});
|
||||||
|
const result = await tool.fn(args, toolStream, this.ai, toolCall.id);
|
||||||
|
return {role: 'tool', tool_call_id: toolCall.id, content: typeof result == 'object' ? JSONSanitize(result) : result, timestamp: Date.now()};
|
||||||
} catch (err: any) {
|
} catch (err: any) {
|
||||||
return {role: 'tool', tool_call_id: toolCall.id, content: JSONSanitize({error: err?.message || err?.toString() || 'Unknown'})};
|
return {role: 'tool', tool_call_id: toolCall.id, content: JSONSanitize({error: err?.message || err?.toString() || 'Unknown'}), timestamp: Date.now()};
|
||||||
}
|
}
|
||||||
}));
|
}));
|
||||||
history.push(...results);
|
history.push(...results);
|
||||||
requestParams.messages = history;
|
requestParams.messages = history;
|
||||||
}
|
}
|
||||||
} while (!controller.signal.aborted && resp.choices?.[0]?.message?.tool_calls?.length);
|
} while (!terminal && !controller.signal.aborted && resp.choices?.[0]?.message?.tool_calls?.length);
|
||||||
|
|
||||||
|
if(!terminal) {
|
||||||
|
const textContent = resp.choices[0].message.content || '';
|
||||||
|
history.push({role: 'assistant', content: textContent.trim(), timestamp: Date.now(), duration, tps});
|
||||||
|
}
|
||||||
|
|
||||||
const textContent = resp.choices[0].message.content?.trim() || '';
|
|
||||||
history.push({role: 'assistant', content: textContent});
|
|
||||||
history = this.toStandard(history);
|
history = this.toStandard(history);
|
||||||
|
if(options.history) options.history.splice(0, options.history.length, ...history.filter(h => h.role !== 'system'));
|
||||||
if(options.stream) options.stream({done: true});
|
if(options.stream) options.stream({done: true});
|
||||||
if(options.history) options.history.splice(0, options.history.length, ...history);
|
|
||||||
|
|
||||||
// Return parsed JSON if schema provided
|
const turnStart = history.map(h => h.role).lastIndexOf('user');
|
||||||
const finalContent = history.at(-1)?.content;
|
const finalContent = history.slice(turnStart + 1).reduce((str, h) => {
|
||||||
|
if(h.role === 'assistant') return str + (h.content || '');
|
||||||
|
return str;
|
||||||
|
}, '').trim();
|
||||||
|
|
||||||
res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
|
res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
|
||||||
}), {abort: () => controller.abort()});
|
}), {abort: () => controller.abort()});
|
||||||
}
|
}
|
||||||
|
|||||||
@@ -1,5 +1,5 @@
|
|||||||
import {AbortablePromise} from './ai.ts';
|
import {AbortablePromise} from './ai.ts';
|
||||||
import {LLMMessage, LLMRequest} from './llm.ts';
|
import {LLMRequest} from './llm.ts';
|
||||||
|
|
||||||
export abstract class LLMProvider {
|
export abstract class LLMProvider {
|
||||||
abstract ask(message: string, options: LLMRequest): AbortablePromise<string>;
|
abstract ask(message: string, options: LLMRequest): AbortablePromise<string>;
|
||||||
|
|||||||
671
src/tools.ts
671
src/tools.ts
@@ -41,7 +41,7 @@ export type AiTool = {
|
|||||||
/** Tool arguments */
|
/** Tool arguments */
|
||||||
args?: AiToolArg,
|
args?: AiToolArg,
|
||||||
/** Callback function */
|
/** Callback function */
|
||||||
fn: (args: any, stream: LLMRequest['stream'], ai: Ai) => any | Promise<any>,
|
fn: (args: any, stream: LLMRequest['stream'], ai: Ai, toolId?: string) => any | Promise<any>,
|
||||||
};
|
};
|
||||||
|
|
||||||
export function convertSchema(schema: any): any {
|
export function convertSchema(schema: any): any {
|
||||||
@@ -91,25 +91,33 @@ export function convertSchema(schema: any): any {
|
|||||||
};
|
};
|
||||||
}
|
}
|
||||||
|
|
||||||
export const CliTool: AiTool = {
|
export const ExecCliTool: AiTool = {
|
||||||
name: 'cli',
|
name: 'cli',
|
||||||
description: 'Use the command line interface, returns any output',
|
description: 'Use the command line interface, returns any output',
|
||||||
args: {command: {type: 'string', description: 'Command to run', required: true}},
|
args: {command: {type: 'string', description: 'Command to run', required: true}},
|
||||||
fn: (args: {command: string}) => $Sync`${args.command}`
|
fn: (args: {command: string}) => $Sync`${args.command}`
|
||||||
}
|
}
|
||||||
|
|
||||||
export const DateTimeTool: AiTool = {
|
export const ExecJSTool: AiTool = {
|
||||||
name: 'get_datetime',
|
name: 'exec_javascript',
|
||||||
description: 'Get local date / time',
|
description: 'Execute commonjs javascript',
|
||||||
args: {},
|
args: {
|
||||||
fn: async () => new Date().toString()
|
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
||||||
|
},
|
||||||
|
fn: async (args: {code: string}) => {
|
||||||
|
const c = consoleInterceptor(null);
|
||||||
|
const resp = await Fn<any>({console: c}, args.code, true).catch((err: any) => c.output.error.push(err));
|
||||||
|
return {...c.output, return: resp, stdout: undefined, stderr: undefined};
|
||||||
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
export const DateTimeUTCTool: AiTool = {
|
export const ExecPythonTool: AiTool = {
|
||||||
name: 'get_datetime_utc',
|
name: 'exec_python',
|
||||||
description: 'Get current UTC date / time',
|
description: 'Execute commonjs javascript',
|
||||||
args: {},
|
args: {
|
||||||
fn: async () => new Date().toUTCString()
|
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
||||||
|
},
|
||||||
|
fn: async (args: {code: string}) => ({result: $Sync`python -c "${args.code}"`})
|
||||||
}
|
}
|
||||||
|
|
||||||
export const ExecTool: AiTool = {
|
export const ExecTool: AiTool = {
|
||||||
@@ -123,11 +131,11 @@ export const ExecTool: AiTool = {
|
|||||||
try {
|
try {
|
||||||
switch(args.language) {
|
switch(args.language) {
|
||||||
case 'cli':
|
case 'cli':
|
||||||
return await CliTool.fn({command: args.code}, stream, ai);
|
return await ExecCliTool.fn({command: args.code}, stream, ai);
|
||||||
case 'node':
|
case 'node':
|
||||||
return await JSTool.fn({code: args.code}, stream, ai);
|
return await ExecJSTool.fn({code: args.code}, stream, ai);
|
||||||
case 'python':
|
case 'python':
|
||||||
return await PythonTool.fn({code: args.code}, stream, ai);
|
return await ExecPythonTool.fn({code: args.code}, stream, ai);
|
||||||
default:
|
default:
|
||||||
throw new Error(`Unsupported language: ${args.language}`);
|
throw new Error(`Unsupported language: ${args.language}`);
|
||||||
}
|
}
|
||||||
@@ -137,8 +145,483 @@ export const ExecTool: AiTool = {
|
|||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
export const FetchTool: AiTool = {
|
export const FsDeleteTool = (whitelist: null | string[] = null): AiTool => {
|
||||||
name: 'fetch',
|
return {
|
||||||
|
name: 'fs_delete',
|
||||||
|
description: 'Delete a file or directory',
|
||||||
|
args: {
|
||||||
|
path: {type: 'string', description: 'Path to file or directory', required: true},
|
||||||
|
recursive: {type: 'boolean', description: 'Delete all children', required: false}
|
||||||
|
},
|
||||||
|
fn: async ({path, recursive = false}) => {
|
||||||
|
const {existsSync, rmSync} = await import('fs');
|
||||||
|
const normalizePath = p => p.replace(/\\/g, '/');
|
||||||
|
|
||||||
|
path = normalizePath(path);
|
||||||
|
if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
|
||||||
|
if(!existsSync(path)) return {error: 'Path does not exist'};
|
||||||
|
|
||||||
|
rmSync(path, {recursive, force: true});
|
||||||
|
return {success: true, path};
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export const FsMoveTool = (whitelist: null | string[] = null): AiTool => {
|
||||||
|
return {
|
||||||
|
name: 'fs_move',
|
||||||
|
description: 'Move or rename a file or directory',
|
||||||
|
args: {
|
||||||
|
source: {type: 'string', description: 'Path to source file or directory', required: true},
|
||||||
|
destination: {type: 'string', description: 'Path to destination file or directory', required: true}
|
||||||
|
},
|
||||||
|
fn: async ({source, destination}) => {
|
||||||
|
const {existsSync, renameSync} = await import('fs');
|
||||||
|
const normalizePath = p => p.replace(/\\/g, '/');
|
||||||
|
|
||||||
|
source = normalizePath(source);
|
||||||
|
destination = normalizePath(destination);
|
||||||
|
if(whitelist && !whitelist.some(p => source.startsWith(p) && destination.startsWith(p))) return {error: 'Permission denied'};
|
||||||
|
|
||||||
|
if(!existsSync(source)) return {error: 'Source path does not exist'};
|
||||||
|
if(existsSync(destination)) return {error: 'Destination path already exists'};
|
||||||
|
|
||||||
|
renameSync(source, destination);
|
||||||
|
return {success: true, source, destination};
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export const FsReadTool = (whitelist: null | string[] = null): AiTool => {
|
||||||
|
return {
|
||||||
|
name: 'fs_read',
|
||||||
|
description: 'Read the contents of a provided path. Works with files and directories',
|
||||||
|
args: {path: {type: 'string', description: 'Path to file or directory', required: true}},
|
||||||
|
fn: async ({path}) => {
|
||||||
|
const {existsSync, lstatSync, readdirSync, readFileSync} = await import('fs');
|
||||||
|
const {join} = await import('path');
|
||||||
|
const normalizePath = p => p.replace(/\\/g, '/');
|
||||||
|
|
||||||
|
path = normalizePath(path);
|
||||||
|
if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
|
||||||
|
|
||||||
|
if(!existsSync(path)) return {error: 'Path does not exist'};
|
||||||
|
const stats = lstatSync(path);
|
||||||
|
if(stats.isDirectory()) {
|
||||||
|
const children = readdirSync(path).map(name => {
|
||||||
|
const childPath = normalizePath(join(path, name));
|
||||||
|
const childStats = lstatSync(childPath);
|
||||||
|
return {name, type: childStats.isDirectory() ? 'directory' : 'file', size: childStats.size};
|
||||||
|
});
|
||||||
|
return {type: 'directory', children};
|
||||||
|
}
|
||||||
|
const content = readFileSync(path, 'utf-8');
|
||||||
|
return {type: 'file', content};
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export const FsSearchTool = (whitelist: null | string[] = null): AiTool => {
|
||||||
|
return {
|
||||||
|
name: 'fs_search',
|
||||||
|
description: 'Scan a directory for matching glob patterns (e.g. "**/*.js", "src/**/*.test.ts")',
|
||||||
|
args: {
|
||||||
|
pattern: {type: 'string', description: 'Glob pattern to match against paths', required: true},
|
||||||
|
root: {type: 'string', description: 'Directory to search from', required: false, default: '.'}
|
||||||
|
},
|
||||||
|
fn: async ({pattern, root = '.'}) => {
|
||||||
|
const {existsSync, lstatSync, readdirSync} = await import('fs');
|
||||||
|
const {join, relative} = await import('path');
|
||||||
|
const normalizePath = p => p.replace(/\\/g, '/');
|
||||||
|
|
||||||
|
root = normalizePath(root);
|
||||||
|
if(!existsSync(root)) return {error: 'Root path does not exist'};
|
||||||
|
if(!lstatSync(root).isDirectory()) return {error: 'Root path is not a directory'};
|
||||||
|
|
||||||
|
if(whitelist && !whitelist.some(p => root.startsWith(p))) return {error: 'Permission denied'};
|
||||||
|
|
||||||
|
const globToRegex = (glob) => {
|
||||||
|
let re = '';
|
||||||
|
for(let i = 0; i < glob.length; i++) {
|
||||||
|
const c = glob[i];
|
||||||
|
if(c === '*') {
|
||||||
|
if(glob[i + 1] === '*') {
|
||||||
|
const isSlash = glob[i + 2] === '/';
|
||||||
|
re += '.*';
|
||||||
|
i += isSlash ? 2 : 1;
|
||||||
|
} else {
|
||||||
|
re += '[^/]*';
|
||||||
|
}
|
||||||
|
} else if(c === '?') {
|
||||||
|
re += '[^/]';
|
||||||
|
} else if('.+^$(){}|[]\\'.includes(c)) {
|
||||||
|
re += '\\' + c;
|
||||||
|
} else {
|
||||||
|
re += c;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
return new RegExp('^' + re + '$');
|
||||||
|
};
|
||||||
|
const regex = globToRegex(pattern);
|
||||||
|
|
||||||
|
const results: any = [];
|
||||||
|
const walk = (dir) => {
|
||||||
|
for(const name of readdirSync(dir)) {
|
||||||
|
const fullPath = normalizePath(join(dir, name));
|
||||||
|
const stats = lstatSync(fullPath);
|
||||||
|
const relPath = normalizePath(relative(root, fullPath));
|
||||||
|
if(regex.test(relPath)) {
|
||||||
|
results.push({path: relPath, type: stats.isDirectory() ? 'directory' : 'file', size: stats.size});
|
||||||
|
}
|
||||||
|
if(stats.isDirectory()) walk(fullPath);
|
||||||
|
}
|
||||||
|
};
|
||||||
|
walk(root);
|
||||||
|
|
||||||
|
return results;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export const FsWriteTool = (whitelist: null | string[] = null): AiTool => {
|
||||||
|
return {
|
||||||
|
name: 'fs_write',
|
||||||
|
description: 'Create a directory, write content to a file or preform a find & replace',
|
||||||
|
args: {
|
||||||
|
path: {type: 'string', description: 'Path to file or directory', required: true},
|
||||||
|
content: {type: 'string', description: 'Content to write or replace (Omit to create a directory)'},
|
||||||
|
find: {type: 'string', description: 'Text or regex pattern to match (regex must match pattern: "/pattern/g")'}
|
||||||
|
},
|
||||||
|
fn: async ({path, content, find}) => {
|
||||||
|
const {existsSync, mkdirSync, readFileSync, writeFileSync} = await import('fs');
|
||||||
|
const {dirname} = await import('path');
|
||||||
|
const normalizePath = p => p.replace(/\\/g, '/');
|
||||||
|
|
||||||
|
path = normalizePath(path);
|
||||||
|
if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
|
||||||
|
|
||||||
|
if(content === undefined) {
|
||||||
|
mkdirSync(path, {recursive: true});
|
||||||
|
return {success: true, type: 'directory', path};
|
||||||
|
}
|
||||||
|
|
||||||
|
const dir = normalizePath(dirname(path));
|
||||||
|
if(!existsSync(dir)) mkdirSync(dir, {recursive: true});
|
||||||
|
|
||||||
|
if(find && existsSync(path)) {
|
||||||
|
const existing = readFileSync(path, 'utf-8');
|
||||||
|
const regexMatch = find.match(/^\/(.+)\/([gimuy]*)$/);
|
||||||
|
const pattern = regexMatch ? new RegExp(regexMatch[1], regexMatch[2]) : find;
|
||||||
|
|
||||||
|
if(!existing.match(pattern)) return {error: 'Find pattern not found in file'};
|
||||||
|
|
||||||
|
const updated = existing.replace(pattern, content);
|
||||||
|
writeFileSync(path, updated, 'utf-8');
|
||||||
|
return {success: true, type: 'file', path, replaced: true, content: updated};
|
||||||
|
}
|
||||||
|
|
||||||
|
writeFileSync(path, content, 'utf-8');
|
||||||
|
return {success: true, type: 'file', path, content};
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export const GetPathsTool: AiTool = {
|
||||||
|
name: 'get_paths',
|
||||||
|
description: 'Get the current working directory, and paths to the users home directory',
|
||||||
|
fn: async () => {
|
||||||
|
return {
|
||||||
|
home: os.homedir(),
|
||||||
|
cwd: process.cwd()
|
||||||
|
};
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export const GetDatetimeTool: AiTool = {
|
||||||
|
name: 'get_datetime',
|
||||||
|
description: 'Get local/UTC timestamp',
|
||||||
|
args: {
|
||||||
|
timezone: {type: 'string', description: 'Which timezone to return, defaults to local', enum: ['local', 'utc'], default: 'local'}
|
||||||
|
},
|
||||||
|
fn: ({timezone}) => new Date()[timezone === 'local' ? 'toString' : 'toUTCString']()
|
||||||
|
}
|
||||||
|
|
||||||
|
export const GetDevice: AiTool = {
|
||||||
|
name: 'get_device',
|
||||||
|
description: 'Get comprehensive system information including hostname, specs, load, storage, and network status',
|
||||||
|
args: {},
|
||||||
|
fn: async () => {
|
||||||
|
const platform = os.platform();
|
||||||
|
const hostname = os.hostname();
|
||||||
|
|
||||||
|
// CPU Info
|
||||||
|
const cpus = os.cpus();
|
||||||
|
const cpuModel = cpus[0].model;
|
||||||
|
const cpuCores = cpus.length;
|
||||||
|
|
||||||
|
// Memory Info
|
||||||
|
const totalMem: any = (os.totalmem() / 1024 / 1024 / 1024).toFixed(2);
|
||||||
|
const freeMem: any = (os.freemem() / 1024 / 1024 / 1024).toFixed(2);
|
||||||
|
const usedMem: any = (totalMem - freeMem).toFixed(2);
|
||||||
|
const memUsage: any = ((usedMem / totalMem) * 100).toFixed(1);
|
||||||
|
|
||||||
|
// Load Average (not available on Windows)
|
||||||
|
const loadAvg = platform === 'win32' ? ['N/A', 'N/A', 'N/A'] : os.loadavg().map(l => l.toFixed(2));
|
||||||
|
|
||||||
|
// Storage Usage
|
||||||
|
let storage = {};
|
||||||
|
if(platform === 'win32') {
|
||||||
|
const ps = $Sync`powershell "Get-PSDrive C | Select-Object Used,Free | ConvertTo-Json"`.trim();
|
||||||
|
const drive = JSON.parse(ps);
|
||||||
|
const used: any = (drive.Used / 1024 / 1024 / 1024).toFixed(2);
|
||||||
|
const free: any = (drive.Free / 1024 / 1024 / 1024).toFixed(2);
|
||||||
|
const total: any = (parseFloat(used) + parseFloat(free)).toFixed(2);
|
||||||
|
const usage: any = ((used / total) * 100).toFixed(1);
|
||||||
|
storage = {
|
||||||
|
filesystem: 'C:',
|
||||||
|
size: `${total} GB`,
|
||||||
|
used: `${used} GB`,
|
||||||
|
available: `${free} GB`,
|
||||||
|
usage: `${usage}%`
|
||||||
|
};
|
||||||
|
} else {
|
||||||
|
const df = $Sync`df -h / | tail -1`.trim();
|
||||||
|
const s = df.split(/\s+/);
|
||||||
|
storage = {
|
||||||
|
filesystem: s[0],
|
||||||
|
size: s[1],
|
||||||
|
used: s[2],
|
||||||
|
available: s[3],
|
||||||
|
usage: s[4]
|
||||||
|
};
|
||||||
|
}
|
||||||
|
|
||||||
|
// Network Status
|
||||||
|
const interfaces = os.networkInterfaces();
|
||||||
|
const activeIfaces = Object.entries(interfaces)
|
||||||
|
.filter(([name]) => name !== 'lo' && !name.includes('Loopback'))
|
||||||
|
.map(([name, addrs]) => {
|
||||||
|
const ipv4 = addrs?.find(a => a.family === 'IPv4');
|
||||||
|
return ipv4 ? {name, ip: ipv4.address} : null;
|
||||||
|
})
|
||||||
|
.filter(Boolean);
|
||||||
|
|
||||||
|
// Internet connectivity check
|
||||||
|
let internet = false;
|
||||||
|
try {
|
||||||
|
if(platform === 'win32') {
|
||||||
|
$Sync`powershell "Test-Connection -ComputerName 8.8.8.8 -Count 1 -Quiet"`;
|
||||||
|
} else {
|
||||||
|
$Sync`ping -c 1 -W 2 8.8.8.8 > /dev/null 2>&1`;
|
||||||
|
}
|
||||||
|
internet = true;
|
||||||
|
} catch {}
|
||||||
|
|
||||||
|
// Uptime
|
||||||
|
const uptime = os.uptime();
|
||||||
|
const days = Math.floor(uptime / 86400);
|
||||||
|
const hours = Math.floor((uptime % 86400) / 3600);
|
||||||
|
const minutes = Math.floor((uptime % 3600) / 60);
|
||||||
|
|
||||||
|
return {
|
||||||
|
hostname,
|
||||||
|
cpu: {
|
||||||
|
model: cpuModel,
|
||||||
|
cores: cpuCores
|
||||||
|
},
|
||||||
|
memory: {
|
||||||
|
total: `${totalMem} GB`,
|
||||||
|
used: `${usedMem} GB`,
|
||||||
|
free: `${freeMem} GB`,
|
||||||
|
usage: `${memUsage}%`
|
||||||
|
},
|
||||||
|
load: {
|
||||||
|
'1min': loadAvg[0],
|
||||||
|
'5min': loadAvg[1],
|
||||||
|
'15min': loadAvg[2]
|
||||||
|
},
|
||||||
|
storage,
|
||||||
|
network: {
|
||||||
|
interfaces: activeIfaces,
|
||||||
|
internet: internet ? 'connected' : 'disconnected'
|
||||||
|
},
|
||||||
|
uptime: `${days}d ${hours}h ${minutes}m`,
|
||||||
|
platform: `${os.type()} ${os.release()}`
|
||||||
|
};
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export const GetWikipediaTool: AiTool = {
|
||||||
|
name: 'get_wikipedia',
|
||||||
|
description: 'Search Wikipedia for matching articles',
|
||||||
|
args: {
|
||||||
|
query: {type: 'string', description: 'Search term or article title', required: true},
|
||||||
|
mode: {type: 'string', description: 'search - look for articles, summary - intro of first found article (default), full - complete first found article', enum: ['search', 'summary', 'full'], default: 'summary'},
|
||||||
|
ua: {type: 'string', description: 'User Agent'},
|
||||||
|
},
|
||||||
|
fn: async ({query, mode, ua}) => {
|
||||||
|
class WikipediaClient {
|
||||||
|
useragent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)';
|
||||||
|
|
||||||
|
constructor(useragent: string) {
|
||||||
|
this.useragent = useragent;
|
||||||
|
}
|
||||||
|
|
||||||
|
async get(url) {
|
||||||
|
const resp = await fetch(url, {headers: {'User-Agent': this.useragent}});
|
||||||
|
return resp.json();
|
||||||
|
}
|
||||||
|
|
||||||
|
api(params) {
|
||||||
|
const qs = new URLSearchParams({...params, format: 'json', utf8: '1'}).toString();
|
||||||
|
return this.get(`https://en.wikipedia.org/w/api.php?${qs}`);
|
||||||
|
}
|
||||||
|
|
||||||
|
clean(text) {
|
||||||
|
const cutoffs = ['== See also ==', '== References ==', '== Bibliography ==', '== External links =='];
|
||||||
|
for (const marker of cutoffs) {
|
||||||
|
const idx = text.indexOf(marker);
|
||||||
|
if (idx !== -1) text = text.slice(0, idx);
|
||||||
|
}
|
||||||
|
|
||||||
|
return text
|
||||||
|
.replace(/^={4}\s*(.+?)\s*={4}$/gm, '#### $1')
|
||||||
|
.replace(/^={3}\s*(.+?)\s*={3}$/gm, '### $1')
|
||||||
|
.replace(/^={2}\s*(.+?)\s*={2}$/gm, '## $1')
|
||||||
|
.replace(/\n{3,}/g, '\n\n')
|
||||||
|
.replace(/ {2,}/g, ' ')
|
||||||
|
.replace(/\[\d+]/g, '')
|
||||||
|
.trim();
|
||||||
|
}
|
||||||
|
|
||||||
|
async searchTitles(query: string, limit = 6) {
|
||||||
|
const data = await this.api({action: 'query', list: 'search', srsearch: query, srlimit: limit, srprop: 'snippet'});
|
||||||
|
return data.query?.search || [];
|
||||||
|
}
|
||||||
|
|
||||||
|
async fetchExtract(title: string, introOnly = false) {
|
||||||
|
const params: any = {action: 'query', prop: 'extracts', titles: title, explaintext: 1, redirects: 1};
|
||||||
|
if(introOnly) params.exintro = 1;
|
||||||
|
const data = await this.api(params);
|
||||||
|
const page: any = Object.values(data.query?.pages || {})[0];
|
||||||
|
return this.clean(page?.extract || '');
|
||||||
|
}
|
||||||
|
|
||||||
|
pageUrl(title: string) {
|
||||||
|
return `https://en.wikipedia.org/wiki/${encodeURIComponent(title.replace(/ /g, '_'))}`;
|
||||||
|
}
|
||||||
|
|
||||||
|
stripHtml(text: string) {
|
||||||
|
return text.replace(/<[^>]+>/g, '');
|
||||||
|
}
|
||||||
|
|
||||||
|
async lookup(query: string, detail = 'summary') {
|
||||||
|
const results = await this.searchTitles(query, 6);
|
||||||
|
if(!results.length) return `❌ No Wikipedia articles found for "${query}"`;
|
||||||
|
const title = results[0].title;
|
||||||
|
const url = this.pageUrl(title);
|
||||||
|
const introOnly = detail !== 'full';
|
||||||
|
const content = await this.fetchExtract(title, introOnly);
|
||||||
|
return `## ${title}\n🔗 ${url}\n\n${content}`;
|
||||||
|
}
|
||||||
|
|
||||||
|
async search(query: string) {
|
||||||
|
const results = await this.searchTitles(query, 8);
|
||||||
|
if(!results.length) return `❌ No results for "${query}"`;
|
||||||
|
const lines = [`### Search results for "${query}"\n`];
|
||||||
|
for(let i = 0; i < results.length; i++) {
|
||||||
|
const r = results[i];
|
||||||
|
const snippet = this.stripHtml(r.snippet || '').trim();
|
||||||
|
lines.push(`**${i + 1}. ${r.title}**\n${snippet}\n${this.pageUrl(r.title)}`);
|
||||||
|
}
|
||||||
|
return lines.join('\n\n');
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
const wiki = new WikipediaClient(ua);
|
||||||
|
if(mode === 'search') return wiki.search(query);
|
||||||
|
return wiki.lookup(query, mode || 'summary');
|
||||||
|
}
|
||||||
|
};
|
||||||
|
|
||||||
|
export const GeoCodeTool: AiTool = {
|
||||||
|
name: 'geo_code',
|
||||||
|
description: 'Converts coordinates to address OR vice versa',
|
||||||
|
args: {
|
||||||
|
query: {type: 'string', description: 'Search query - coordinates (lat,lon) or address string', required: true},
|
||||||
|
},
|
||||||
|
fn: async ({query}) => {
|
||||||
|
const coordinates = /(-?\d+(?:\.\d+)?).*?,.*?(-?\d+(?:\.\d+)?)/.exec(query);
|
||||||
|
if(coordinates) { // Geolocate
|
||||||
|
const url = `https://nominatim.openstreetmap.org/reverse?format=json&lat=${encodeURIComponent(coordinates[1])}&lon=${encodeURIComponent(coordinates[2])}`;
|
||||||
|
const response = await fetch(url, {headers: {'User-Agent': 'OpenSight/1.0', 'Accept-Language': 'en'}});
|
||||||
|
const data = await response.json();
|
||||||
|
if(data.display_name) return {address: data.display_name, mode: 'geolocate'};
|
||||||
|
} else { // Geocode
|
||||||
|
const url = `https://nominatim.openstreetmap.org/search?format=json&q=${encodeURIComponent(query)}`;
|
||||||
|
const response = await fetch(url, {headers: {'User-Agent': 'OpenSight/1.0'}});
|
||||||
|
const data = await response.json();
|
||||||
|
if(data[0]) return {latitude: parseFloat(data[0].lat), longitude: parseFloat(data[0].lon), mode: 'geocode'};
|
||||||
|
}
|
||||||
|
return {error: 'Not found'};
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
export const GeoWeatherTool: AiTool = {
|
||||||
|
name: 'geo_weather',
|
||||||
|
description: 'Gets weather and air quality info for a location and time',
|
||||||
|
args: {
|
||||||
|
query: {type: 'string', description: 'Location - address or place name', required: true},
|
||||||
|
day: {type: 'string', description: 'Date to retrieve (YYYY-MM-DD), defaults to today'},
|
||||||
|
},
|
||||||
|
fn: async ({query, day}) => {
|
||||||
|
day = day || new Date().toISOString().slice(0, 10);
|
||||||
|
|
||||||
|
const geoUrl = `https://nominatim.openstreetmap.org/search?format=json&q=${encodeURIComponent(query)}`;
|
||||||
|
const geoResponse = await fetch(geoUrl, {headers: {'User-Agent': 'OpenSight/1.0'}});
|
||||||
|
const geoData = await geoResponse.json();
|
||||||
|
if(!geoData[0]) return {error: 'Location not found'};
|
||||||
|
|
||||||
|
const lat = parseFloat(geoData[0].lat);
|
||||||
|
const lon = parseFloat(geoData[0].lon);
|
||||||
|
|
||||||
|
const weatherUrl = `https://api.open-meteo.com/v1/forecast?latitude=${lat}&longitude=${lon}&start_date=${day}&end_date=${day}&daily=weathercode,temperature_2m_max,temperature_2m_min,apparent_temperature_max,apparent_temperature_min,precipitation_sum,precipitation_probability_max,windspeed_10m_max,winddirection_10m_dominant,uv_index_max,sunrise,sunset&timezone=auto`;
|
||||||
|
const airUrl = `https://air-quality-api.open-meteo.com/v1/air-quality?latitude=${lat}&longitude=${lon}&start_date=${day}&end_date=${day}&hourly=us_aqi,european_aqi,pm10,pm2_5&timezone=auto`;
|
||||||
|
|
||||||
|
const [weatherResponse, airResponse] = await Promise.all([fetch(weatherUrl), fetch(airUrl)]);
|
||||||
|
const weatherData = await weatherResponse.json();
|
||||||
|
const airData = await airResponse.json();
|
||||||
|
|
||||||
|
const avg = arr => (arr && arr.length) ? arr.reduce((a, b) => a + b, 0) / arr.length : null;
|
||||||
|
|
||||||
|
return {
|
||||||
|
location: geoData[0].display_name,
|
||||||
|
latitude: lat,
|
||||||
|
longitude: lon,
|
||||||
|
elevation: weatherData.elevation,
|
||||||
|
date: day,
|
||||||
|
weatherCode: weatherData.daily?.weathercode?.[0],
|
||||||
|
tempMax: weatherData.daily?.temperature_2m_max?.[0],
|
||||||
|
tempMin: weatherData.daily?.temperature_2m_min?.[0],
|
||||||
|
feelsLikeMax: weatherData.daily?.apparent_temperature_max?.[0],
|
||||||
|
feelsLikeMin: weatherData.daily?.apparent_temperature_min?.[0],
|
||||||
|
precipitation: weatherData.daily?.precipitation_sum?.[0],
|
||||||
|
precipitationChance: weatherData.daily?.precipitation_probability_max?.[0],
|
||||||
|
windSpeedMax: weatherData.daily?.windspeed_10m_max?.[0],
|
||||||
|
windDirection: weatherData.daily?.winddirection_10m_dominant?.[0],
|
||||||
|
uvIndexMax: weatherData.daily?.uv_index_max?.[0],
|
||||||
|
sunrise: weatherData.daily?.sunrise?.[0],
|
||||||
|
sunset: weatherData.daily?.sunset?.[0],
|
||||||
|
usAqi: avg(airData.hourly?.us_aqi),
|
||||||
|
europeanAqi: avg(airData.hourly?.european_aqi),
|
||||||
|
pm10: avg(airData.hourly?.pm10),
|
||||||
|
pm2_5: avg(airData.hourly?.pm2_5),
|
||||||
|
};
|
||||||
|
},
|
||||||
|
}
|
||||||
|
|
||||||
|
export const WebFetchTool: AiTool = {
|
||||||
|
name: 'web_fetch',
|
||||||
description: 'Make HTTP request to URL',
|
description: 'Make HTTP request to URL',
|
||||||
args: {
|
args: {
|
||||||
url: {type: 'string', description: 'URL to fetch', required: true},
|
url: {type: 'string', description: 'URL to fetch', required: true},
|
||||||
@@ -154,30 +637,59 @@ export const FetchTool: AiTool = {
|
|||||||
}) => new Http({url: args.url, headers: args.headers}).request({method: args.method || 'GET', body: args.body})
|
}) => new Http({url: args.url, headers: args.headers}).request({method: args.method || 'GET', body: args.body})
|
||||||
}
|
}
|
||||||
|
|
||||||
export const JSTool: AiTool = {
|
export const WebFlareSolverTool = (host: string) => {
|
||||||
name: 'exec_javascript',
|
return {
|
||||||
description: 'Execute commonjs javascript',
|
name: 'web_flaresolverr',
|
||||||
|
description: 'Use a flaresolverr proxy to bypass cloudflare bot detection',
|
||||||
args: {
|
args: {
|
||||||
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
url: {type: 'string', description: 'URL to fetch', required: true},
|
||||||
|
cmd: {type: 'string', description: 'Flaresolverr cmd', enum: ['request.get', 'request.post'], default: 'request.get'},
|
||||||
|
maxTimeout: {type: 'number', description: 'Fetch time limit', default: 60_000},
|
||||||
|
postData: {type: 'object', description: 'Data to send during request.post requests'},
|
||||||
},
|
},
|
||||||
fn: async (args: {code: string}) => {
|
fn: async ({url, cmd, maxTimeout, postData}) => {
|
||||||
const c = consoleInterceptor(null);
|
function toFormUrlEncoded(obj, prefix = '') {
|
||||||
const resp = await Fn<any>({console: c}, args.code, true).catch((err: any) => c.output.error.push(err));
|
const pairs: any = [];
|
||||||
return {...c.output, return: resp, stdout: undefined, stderr: undefined};
|
for (const key in obj) {
|
||||||
|
if (!obj.hasOwnProperty(key)) continue;
|
||||||
|
|
||||||
|
const value = obj[key];
|
||||||
|
const encodedKey = prefix
|
||||||
|
? `${prefix}[${encodeURIComponent(key)}]`
|
||||||
|
: encodeURIComponent(key);
|
||||||
|
|
||||||
|
if (value === null || value === undefined) {
|
||||||
|
pairs.push(`${encodedKey}=`);
|
||||||
|
} else if (typeof value === 'object' && !Array.isArray(value)) {
|
||||||
|
pairs.push(toFormUrlEncoded(value, encodedKey));
|
||||||
|
} else if (Array.isArray(value)) {
|
||||||
|
value.forEach(item => {
|
||||||
|
pairs.push(`${encodedKey}[]=${encodeURIComponent(item)}`);
|
||||||
|
});
|
||||||
|
} else {
|
||||||
|
pairs.push(`${encodedKey}=${encodeURIComponent(value)}`);
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
export const PythonTool: AiTool = {
|
return pairs.join('&');
|
||||||
name: 'exec_javascript',
|
|
||||||
description: 'Execute commonjs javascript',
|
|
||||||
args: {
|
|
||||||
code: {type: 'string', description: 'CommonJS javascript', required: true}
|
|
||||||
},
|
|
||||||
fn: async (args: {code: string}) => ({result: $Sync`python -c "${args.code}"`})
|
|
||||||
}
|
}
|
||||||
|
|
||||||
export const ReadWebpageTool: AiTool = {
|
const res = await fetch(host + '/v1', {
|
||||||
name: 'read_webpage',
|
method: 'POST',
|
||||||
|
headers: {'Content-Type': 'application/json'},
|
||||||
|
body: JSON.stringify({cmd, url, maxTimeout, postData: postData ? toFormUrlEncoded(postData) : undefined}),
|
||||||
|
});
|
||||||
|
|
||||||
|
if(!res.ok) throw new Error(`FlareSolverr HTTP error: ${res.status} ${res.statusText}`);
|
||||||
|
const data = await res.json();
|
||||||
|
if(data.status !== 'ok') throw new Error(`FlareSolverr error: ${data.message ?? data.status}`);
|
||||||
|
return data.solution.response;
|
||||||
|
}
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
export const WebReadTool: AiTool = {
|
||||||
|
name: 'web_read',
|
||||||
description: 'Extract clean content from webpages, or convert media/documents to accessible formats',
|
description: 'Extract clean content from webpages, or convert media/documents to accessible formats',
|
||||||
args: {
|
args: {
|
||||||
url: {type: 'string', description: 'URL to read', required: true},
|
url: {type: 'string', description: 'URL to read', required: true},
|
||||||
@@ -305,94 +817,3 @@ export const WebSearchTool: AiTool = {
|
|||||||
return results;
|
return results;
|
||||||
}
|
}
|
||||||
}
|
}
|
||||||
|
|
||||||
class WikipediaClient {
|
|
||||||
private async get(url: string): Promise<any> {
|
|
||||||
const resp = await fetch(url, {headers: {'User-Agent': UA}});
|
|
||||||
return resp.json();
|
|
||||||
}
|
|
||||||
|
|
||||||
private api(params: Record<string, any>): Promise<any> {
|
|
||||||
const qs = new URLSearchParams({...params, format: 'json', utf8: '1'}).toString();
|
|
||||||
return this.get(`https://en.wikipedia.org/w/api.php?${qs}`);
|
|
||||||
}
|
|
||||||
|
|
||||||
private clean(text: string): string {
|
|
||||||
return text.replace(/\n{3,}/g, '\n\n').replace(/ {2,}/g, ' ').replace(/\[\d+\]/g, '').trim();
|
|
||||||
}
|
|
||||||
|
|
||||||
private truncate(text: string, max: number): string {
|
|
||||||
if(text.length <= max) return text;
|
|
||||||
const cut = text.slice(0, max);
|
|
||||||
const lastPara = cut.lastIndexOf('\n\n');
|
|
||||||
return lastPara > max * 0.7 ? cut.slice(0, lastPara) : cut;
|
|
||||||
}
|
|
||||||
|
|
||||||
private async searchTitles(query: string, limit = 6): Promise<any[]> {
|
|
||||||
const data = await this.api({action: 'query', list: 'search', srsearch: query, srlimit: limit, srprop: 'snippet'});
|
|
||||||
return data.query?.search || [];
|
|
||||||
}
|
|
||||||
|
|
||||||
private async fetchExtract(title: string, intro = false): Promise<string> {
|
|
||||||
const params: any = {action: 'query', prop: 'extracts', titles: title, explaintext: 1, redirects: 1};
|
|
||||||
if(intro) params.exintro = 1;
|
|
||||||
const data = await this.api(params);
|
|
||||||
const page = Object.values(data.query?.pages || {})[0] as any;
|
|
||||||
return this.clean(page?.extract || '');
|
|
||||||
}
|
|
||||||
|
|
||||||
private pageUrl(title: string): string {
|
|
||||||
return `https://en.wikipedia.org/wiki/${encodeURIComponent(title.replace(/ /g, '_'))}`;
|
|
||||||
}
|
|
||||||
|
|
||||||
private stripHtml(text: string): string {
|
|
||||||
return text.replace(/<[^>]+>/g, '');
|
|
||||||
}
|
|
||||||
|
|
||||||
async lookup(query: string, detail: 'intro' | 'full' = 'intro'): Promise<string> {
|
|
||||||
const results = await this.searchTitles(query, 6);
|
|
||||||
if(!results.length) return `❌ No Wikipedia articles found for "${query}"`;
|
|
||||||
const title = results[0].title;
|
|
||||||
const url = this.pageUrl(title);
|
|
||||||
const content = await this.fetchExtract(title, detail === 'intro');
|
|
||||||
const text = this.truncate(content, detail === 'intro' ? 2000 : 8000);
|
|
||||||
return `## ${title}\n🔗 ${url}\n\n${text}`;
|
|
||||||
}
|
|
||||||
|
|
||||||
async search(query: string): Promise<string> {
|
|
||||||
const results = await this.searchTitles(query, 8);
|
|
||||||
if(!results.length) return `❌ No results for "${query}"`;
|
|
||||||
const lines = [`### Search results for "${query}"\n`];
|
|
||||||
for(let i = 0; i < results.length; i++) {
|
|
||||||
const r = results[i];
|
|
||||||
const snippet = this.truncate(this.stripHtml(r.snippet || ''), 150);
|
|
||||||
lines.push(`**${i + 1}. ${r.title}**\n${snippet}\n${this.pageUrl(r.title)}`);
|
|
||||||
}
|
|
||||||
return lines.join('\n\n');
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
export const WikipediaLookupTool: AiTool = {
|
|
||||||
name: 'wikipedia_lookup',
|
|
||||||
description: 'Get Wikipedia article content',
|
|
||||||
args: {
|
|
||||||
query: {type: 'string', description: 'Topic or article title', required: true},
|
|
||||||
detail: {type: 'string', description: 'Content level: "intro" (summary, default) or "full" (complete article)', enum: ['intro', 'full'], default: 'intro'}
|
|
||||||
},
|
|
||||||
fn: async (args: {query: string; detail?: 'intro' | 'full'}) => {
|
|
||||||
const wiki = new WikipediaClient();
|
|
||||||
return wiki.lookup(args.query, args.detail || 'intro');
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|
||||||
export const WikipediaSearchTool: AiTool = {
|
|
||||||
name: 'wikipedia_search',
|
|
||||||
description: 'Search Wikipedia for matching articles',
|
|
||||||
args: {
|
|
||||||
query: {type: 'string', description: 'Search terms', required: true}
|
|
||||||
},
|
|
||||||
fn: async (args: {query: string}) => {
|
|
||||||
const wiki = new WikipediaClient();
|
|
||||||
return wiki.search(args.query);
|
|
||||||
}
|
|
||||||
};
|
|
||||||
|
|||||||
Reference in New Issue
Block a user