Added memory system

src/llm.ts

@@ -31,11 +31,23 @@ export type LLMMessage = {
 	/** Tool result */
 	content: undefined | string;
 	/** Tool error */
-	error: undefined | string;
+	error?: undefined | string;
 	/** Timestamp */
 	timestamp?: number;
 }
 
+/** Background information the AI will be fed */
+export type LLMMemory = {
+	/** What entity is this fact about */
+	owner: string;
+	/** The information that will be remembered */
+	fact: string;
+	/** Owner and fact embedding vector */
+	embeddings: [number[], number[]];
+	/** Creation time */
+	timestamp: Date;
+}
+
 export type LLMRequest = {
 	/** System prompt */
 	system?: string;
@@ -57,10 +69,12 @@ export type LLMRequest = {
 		max: number;
 		/** Compress chat until context size smaller than */
 		min: number
-	}
+	},
+	/** Background information the AI will be fed */
+	memory?: LLMMemory[],
 }
 
-export class LLM {
+class LLM {
 	private embedWorker: Worker | null = null;
 	private embedQueue = new Map<number, { resolve: (value: number[]) => void; reject: (error: any) => void }>();
 	private embedId = 0;
@@ -90,37 +104,115 @@ export class LLM {
 	 * Chat with LLM
 	 * @param {string} message Question
 	 * @param {LLMRequest} options Configuration options and chat history
-	 * @returns {{abort: () => void, response: Promise<LLMMessage[]>}} Function to abort response and chat history
+	 * @returns {{abort: () => void, response: Promise<string>}} Function to abort response and chat history
 	 */
-	ask(message: string, options: LLMRequest = {}): AbortablePromise<LLMMessage[]> {
+	ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
 		const m = options.model || this.defaultModel;
 		if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
-		return this.models[m].ask(message, options);
+		let abort = () => {};
+		return Object.assign(new Promise<string>(async res => {
+			if(!options.history) options.history = [];
+			// If memories were passed, find any relivant ones and add a tool for ADHOC lookups
+			if(options.memory) {
+				options.system = (options.system || '') + '\nYou have passive persistent memory never make any mention of your memory capabilities and what you can/cannot remember\n';
+				const search = async (query?: string | null, subject?: string | null, limit = 50) => {
+					const [o, q] = await Promise.all([
+						subject ? this.embedding(subject) : Promise.resolve(null),
+						query ? this.embedding(query) : Promise.resolve(null),
+					]);
+					return (options.memory || [])
+						.map(m => ({...m, score: o ? this.cosineSimilarity(m.embeddings[0], o[0].embedding) : 1}))
+						.filter((m: any) => m.score >= 0.8)
+						.map((m: any) => ({...m, score: q ? this.cosineSimilarity(m.embeddings[1], q[0].embedding) : m.score}))
+						.filter((m: any) => m.score >= 0.2)
+						.toSorted((a: any, b: any) => a.score - b.score)
+						.slice(0, limit);
+				}
+
+				const relevant = await search(message);
+				if(relevant.length) options.history.push({role: 'assistant', content: 'Things I remembered:\n' + relevant.map(m => `${m.owner}: ${m.fact}`).join('\n')});
+				options.tools = [...options.tools || [], {
+					name: 'read_memory',
+					description: 'Check your long-term memory for more information',
+					args: {
+						subject: {type: 'string', description: 'Find information by a subject topic, can be used with or without query argument'},
+						query: {type: 'string', description: 'Search memory based on a query, can be used with or without subject argument'},
+						limit: {type: 'number', description: 'Result limit, default 5'},
+					},
+					fn: (args) => {
+						if(!args.subject && !args.query) throw new Error('Either a subject or query argument is required');
+						return search(args.query, args.subject, args.limit || 5);
+					}
+				}];
+			}
+
+			// Ask
+			const resp = await this.models[m].ask(message, options);
+
+			// Remove any memory calls
+			if(options.memory) {
+				const i = options.history?.findIndex((h: any) => h.role == 'assistant' && h.content.startsWith('Things I remembered:'));
+				if(i != null && i >= 0) options.history?.splice(i, 1);
+			}
+
+			// Handle compression and memory extraction
+			if(options.compress || options.memory) {
+				let compressed = null;
+				if(options.compress) {
+					compressed = await this.ai.language.compressHistory(options.history, options.compress.max, options.compress.min, options);
+					options.history.splice(0, options.history.length, ...compressed.history);
+				} else {
+					const i = options.history?.findLastIndex(m => m.role == 'user') ?? -1;
+					compressed = await this.ai.language.compressHistory(i != -1 ? options.history.slice(i) : options.history, 0, 0, options);
+				}
+				if(options.memory) {
+					const updated = options.memory
+						.filter(m => !compressed.memory.some(m2 => this.cosineSimilarity(m.embeddings[1], m2.embeddings[1]) > 0.8))
+						.concat(compressed.memory);
+					options.memory.splice(0, options.memory.length, ...updated);
+				}
+			}
+			return res(resp);
+		}), {abort});
 	}
 
 	/**
 	 * Compress chat history to reduce context size
 	 * @param {LLMMessage[]} history Chatlog that will be compressed
 	 * @param max Trigger compression once context is larger than max
-	 * @param min Summarize until context size is less than min
+	 * @param min Leave messages less than the token minimum, summarize the rest
 	 * @param {LLMRequest} options LLM options
 	 * @returns {Promise<LLMMessage[]>} New chat history will summary at index 0
 	 */
-	async compressHistory(history: LLMMessage[], max: number, min: number, options?: LLMRequest): Promise<LLMMessage[]> {
-		if(this.estimateTokens(history) < max) return history;
+	async compressHistory(history: LLMMessage[], max: number, min: number, options?: LLMRequest): Promise<{history: LLMMessage[], memory: LLMMemory[]}> {
+		if(this.estimateTokens(history) < max) return {history, memory: []};
 		let keep = 0, tokens = 0;
 		for(let m of history.toReversed()) {
 			tokens += this.estimateTokens(m.content);
 			if(tokens < min) keep++;
 			else break;
 		}
-		if(history.length <= keep) return history;
-		const recent = keep == 0 ? [] : history.slice(-keep),
+		if(history.length <= keep) return {history, memory: []};
+		const system = history[0].role == 'system' ? history[0] : null,
+			recent = keep == 0 ? [] : history.slice(-keep),
 			process = (keep == 0 ? history : history.slice(0, -keep)).filter(h => h.role === 'assistant' || h.role === 'user');
-		const summary = await this.summarize(process.map(m => `${m.role}: ${m.content}`).join('\n\n'), 250, options);
-		return [{role: 'assistant', content: `Conversation Summary: ${summary}`, timestamp: Date.now()}, ...recent];
+		const summary: any = await this.json(`Create the smallest summary possible, no more than 500 tokens. Create a list of NEW facts (split by subject [pro]noun and fact) about what you learned from this conversation that you didn't already know or get from a tool call or system prompt. Focus only on new information about people, topics, or facts. Avoid generating facts about the AI. Match this format: {summary: string, facts: [[subject, fact]]}\n\n${process.map(m => `${m.role}: ${m.content}`).join('\n\n')}`, {model: options?.model, temperature: options?.temperature || 0.3});
+		const timestamp = new Date();
+		const memory = await Promise.all((summary?.facts || [])?.map(async ([owner, fact]: [string, string]) => {
+			const e = await Promise.all([this.embedding(owner), this.embedding(`${owner}: ${fact}`)]);
+			return {owner, fact, embeddings: [e[0][0].embedding, e[1][0].embedding], timestamp};
+		}));
+		const h = [{role: 'assistant', content: `Conversation Summary: ${summary?.summary}`, timestamp: Date.now()}, ...recent];
+		if(system) h.splice(0, 0, system);
+		return {history: <any>h, memory};
 	}
 
+	/**
+	 * Compare the difference between embeddings (calculates the angle between two vectors)
+	 * @param {number[]} v1 First embedding / vector comparison
+	 * @param {number[]} v2 Second embedding / vector for comparison
+	 * @returns {number} Similarity values 0-1: 0 = unique, 1 = identical
+	 */
 	cosineSimilarity(v1: number[], v2: number[]): number {
 		if (v1.length !== v2.length) throw new Error('Vectors must be same length');
 		let dotProduct = 0, normA = 0, normB = 0;
@@ -133,6 +225,13 @@ export class LLM {
 		return denominator === 0 ? 0 : dotProduct / denominator;
 	}
 
+	/**
+	 * Chunk text into parts for AI digestion
+	 * @param {object | string} target Item that will be chunked (objects get converted)
+	 * @param {number} maxTokens Chunking size. More = better context, less = more specific (Search by paragraphs or lines)
+	 * @param {number} overlapTokens Includes previous X tokens to provide continuity to AI (In addition to max tokens)
+	 * @returns {string[]} Chunked strings
+	 */
 	chunk(target: object | string, maxTokens = 500, overlapTokens = 50): string[] {
 		const objString = (obj: any, path = ''): string[] => {
 			if(!obj) return [];
@@ -142,7 +241,6 @@ export class LLM {
 				return `${p}: ${Array.isArray(value) ? value.join(', ') : value}`;
 			});
 		};
-
 		const lines = typeof target === 'object' ? objString(target) : target.split('\n');
 		const tokens = lines.flatMap(l => [...l.split(/\s+/).filter(Boolean), '\n']);
 		const chunks: string[] = [];
@@ -161,6 +259,13 @@ export class LLM {
 		return chunks;
 	}
 
+	/**
+	 * Create a vector representation of a string
+	 * @param {object | string} target Item that will be embedded (objects get converted)
+	 * @param {number} maxTokens Chunking size. More = better context, less = more specific (Search by paragraphs or lines)
+	 * @param {number} overlapTokens Includes previous X tokens to provide continuity to AI (In addition to max tokens)
+	 * @returns {Promise<Awaited<{index: number, embedding: number[], text: string, tokens: number}>[]>} Chunked embeddings
+	 */
 	embedding(target: object | string, maxTokens = 500, overlapTokens = 50) {
 		const embed = (text: string): Promise<number[]> => {
 			return new Promise((resolve, reject) => {
@@ -169,7 +274,6 @@ export class LLM {
 				this.embedWorker?.postMessage({ id, text });
 			});
 		};
-
 		const chunks = this.chunk(target, maxTokens, overlapTokens);
 		return Promise.all(chunks.map(async (text, index) => ({
 			index,
@@ -191,7 +295,7 @@ export class LLM {
 
 	/**
 	 * Compare the difference between two strings using tensor math
-	 * @param target Text that will checked
+	 * @param target Text that will be 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
 	 */
@@ -212,13 +316,12 @@ export class LLM {
 	 * @param {LLMRequest} options Configuration options and chat history
 	 * @returns {Promise<{} | {} | RegExpExecArray | null>}
 	 */
-	async json(message: string, options?: LLMRequest) {
-		let resp = await this.ask(message, {
-			system: 'Respond using a JSON blob',
-			...options
-		});
-		if(!resp?.[0]?.content) return {};
-		return JSONAttemptParse(new RegExp('\{[\s\S]*\}').exec(resp[0].content), {});
+	async json(message: string, options?: LLMRequest): Promise<any> {
+		let resp = await this.ask(message, {system: 'Respond using a JSON blob matching any provided examples', ...options});
+		if(!resp) return {};
+		const codeBlock = /```(?:.+)?\s*([\s\S]*?)```/.exec(resp);
+		const jsonStr = codeBlock ? codeBlock[1].trim() : resp;
+		return JSONAttemptParse(jsonStr, {});
 	}
 
 	/**
@@ -229,7 +332,8 @@ export class LLM {
 	 * @returns {Promise<string>} Summary
 	 */
 	summarize(text: string, tokens: number, options?: LLMRequest): Promise<string | null> {
-		return this.ask(text, {system: `Generate a brief summary <= ${tokens} tokens. Output nothing else`, temperature: 0.3, ...options})
-			.then(history => <string>history.pop()?.content || null);
+		return this.ask(text, {system: `Generate a brief summary <= ${tokens} tokens. Output nothing else`, temperature: 0.3, ...options});
 	}
 }
+
+export default LLM;