Re-organized functions and added semantic embeddings

src/llm.ts

@@ -1,3 +1,4 @@
+import {pipeline} from '@xenova/transformers';
 import {JSONAttemptParse} from '@ztimson/utils';
 import {Ai} from './ai.ts';
 import {Anthropic} from './antrhopic.ts';
@@ -5,6 +6,7 @@ import {Ollama} from './ollama.ts';
 import {OpenAi} from './open-ai.ts';
 import {AbortablePromise, LLMProvider} from './provider.ts';
 import {AiTool} from './tools.ts';
+import * as tf from '@tensorflow/tfjs';
 
 export type LLMMessage = {
 	/** Message originator */
@@ -81,12 +83,14 @@ export type LLMRequest = {
 }
 
 export class LLM {
+	private embedModel: any;
 	private providers: {[key: string]: LLMProvider} = {};
 
-	constructor(public readonly ai: Ai, public readonly options: LLMOptions) {
-		if(options.anthropic?.token) this.providers.anthropic = new Anthropic(this.ai, options.anthropic.token, options.anthropic.model);
-		if(options.ollama?.host) this.providers.ollama = new Ollama(this.ai, options.ollama.host, options.ollama.model);
-		if(options.openAi?.token) this.providers.openAi = new OpenAi(this.ai, options.openAi.token, options.openAi.model);
+	constructor(public readonly ai: Ai) {
+		this.embedModel = pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+		if(ai.options.anthropic?.token) this.providers.anthropic = new Anthropic(this.ai, ai.options.anthropic.token, ai.options.anthropic.model);
+		if(ai.options.ollama?.host) this.providers.ollama = new Ollama(this.ai, ai.options.ollama.host, ai.options.ollama.model);
+		if(ai.options.openAi?.token) this.providers.openAi = new OpenAi(this.ai, ai.options.openAi.token, ai.options.openAi.model);
 	}
 
 	/**
@@ -99,11 +103,11 @@ export class LLM {
 		let model: any = [null, null];
 		if(options.model) {
 			if(typeof options.model == 'object') model = options.model;
-			else model = [options.model, (<any>this.options)[options.model]?.model];
+			else model = [options.model, (<any>this.ai.options)[options.model]?.model];
 		}
 		if(!options.model || model[1] == null) {
-			if(typeof this.options.model == 'object') model = this.options.model;
-			else model = [this.options.model, (<any>this.options)[this.options.model]?.model];
+			if(typeof this.ai.options.model == 'object') model = this.ai.options.model;
+			else model = [this.ai.options.model, (<any>this.ai.options)[this.ai.options.model]?.model];
 		}
 		if(!model[0] || !model[1]) throw new Error(`Unknown LLM provider or model: ${model[0]} / ${model[1]}`);
 		return this.providers[model[0]].ask(message, {...options, model: model[1]});
@@ -117,7 +121,7 @@ export class LLM {
 	 * @param {LLMRequest} options LLM options
 	 * @returns {Promise<LLMMessage[]>} New chat history will summary at index 0
 	 */
-	async compress(history: LLMMessage[], max: number, min: number, options?: LLMRequest): Promise<LLMMessage[]> {
+	async compressHistory(history: LLMMessage[], max: number, min: number, options?: LLMRequest): Promise<LLMMessage[]> {
 		if(this.estimateTokens(history) < max) return history;
 		let keep = 0, tokens = 0;
 		for(let m of history.toReversed()) {
@@ -132,6 +136,57 @@ export class LLM {
 		return [{role: 'assistant', content: `Conversation Summary: ${summary}`, timestamp: Date.now()}, ...recent];
 	}
 
+	embedding(target: object | string, maxTokens = 500, overlapTokens = 50) {
+		const objString = (obj: any, path = ''): string[] => {
+			if(obj === null || obj === undefined) return [];
+			return Object.entries(obj).flatMap(([key, value]) => {
+				const p = path ? `${path}${isNaN(+key) ? `.${key}` : `[${key}]`}` : key;
+				if(typeof value === 'object' && value !== null && !Array.isArray(value)) return objString(value, p);
+				const valueStr = Array.isArray(value) ? value.join(', ') : String(value);
+				return `${p}: ${valueStr}`;
+			});
+		};
+
+		const embed = async (text: string): Promise<number[]> => {
+			const model = await this.embedModel;
+			const output = await model(text, {pooling: 'mean', normalize: true});
+			return Array.from(output.data);
+		};
+
+		// Tokenize
+		const lines = typeof target === 'object' ? objString(target) : target.split('\n');
+		const tokens = lines.flatMap(line => [...line.split(/\s+/).filter(w => w.trim()), '\n']);
+
+		// Chunk
+		const chunks: string[] = [];
+		let start = 0;
+		while (start < tokens.length) {
+			let end = start;
+			let text = '';
+			// Build chunk
+			while (end < tokens.length) {
+				const nextToken = tokens[end];
+				const testText = text + (text ? ' ' : '') + nextToken;
+				const testTokens = this.estimateTokens(testText.replace(/\s*\n\s*/g, '\n'));
+				if (testTokens > maxTokens && text) break;
+				text = testText;
+				end++;
+			}
+			// Save chunk
+			const cleanText = text.replace(/\s*\n\s*/g, '\n').trim();
+			if(cleanText) chunks.push(cleanText);
+			start = end - overlapTokens;
+			if (start <= end - tokens.length + end) start = end; // Safety: prevent infinite loop
+		}
+
+		return Promise.all(chunks.map(async (text, index) => ({
+			index,
+			embedding: await embed(text),
+			text,
+			tokens: this.estimateTokens(text),
+		})));
+	}
+
 	/**
 	 * Estimate variable as tokens
 	 * @param history Object to size
@@ -142,6 +197,35 @@ export class LLM {
 		return Math.ceil((text.length / 4) * 1.2);
 	}
 
+	/**
+	 * Compare the difference between two strings using tensor math
+	 * @param target Text that will checked
+	 * @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
+	 */
+	fuzzyMatch(target: string, ...searchTerms: string[]) {
+		if(searchTerms.length < 2) throw new Error('Requires at least 2 strings to compare');
+
+		const vector = (text: string, dimensions: number = 10): number[] => {
+			return text.toLowerCase().split('').map((char, index) =>
+				(char.charCodeAt(0) * (index + 1)) % dimensions / dimensions).slice(0, dimensions);
+		}
+
+		const cosineSimilarity = (v1: number[], v2: number[]): number => {
+			if (v1.length !== v2.length) throw new Error('Vectors must be same length');
+			const tensor1 = tf.tensor1d(v1), tensor2 = tf.tensor1d(v2)
+			const dotProduct = tf.dot(tensor1, tensor2)
+			const magnitude1 = tf.norm(tensor1)
+			const magnitude2 = tf.norm(tensor2)
+			if(magnitude1.dataSync()[0] === 0 || magnitude2.dataSync()[0] === 0) return 0
+			return dotProduct.dataSync()[0] / (magnitude1.dataSync()[0] * magnitude2.dataSync()[0])
+		}
+
+		const v = vector(target);
+		const similarities = searchTerms.map(t => vector(t)).map(refVector => cosineSimilarity(v, refVector))
+		return {avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length, max: Math.max(...similarities), similarities}
+	}
+
 	/**
 	 * Ask a question with JSON response
 	 * @param {string} message Question