Improved memory recall results

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "0.8.0",
+	"version": "0.8.9",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",

src/audio.ts

@@ -35,6 +35,121 @@ print(json.dumps(segments))
 `;
 	}
 
+	private async addPunctuation(timestampData: any, llm?: boolean, cadence = 150): Promise<string> {
+		const countSyllables = (word: string): number => {
+			word = word.toLowerCase().replace(/[^a-z]/g, '');
+			if(word.length <= 3) return 1;
+			const matches = word.match(/[aeiouy]+/g);
+			let count = matches ? matches.length : 1;
+			if(word.endsWith('e')) count--;
+			return Math.max(1, count);
+		};
+
+		let result = '';
+		timestampData.transcription.filter((word, i) => {
+			let skip = false;
+			const prevWord = timestampData.transcription[i - 1];
+			const nextWord = timestampData.transcription[i + 1];
+			if(!word.text && nextWord) {
+				nextWord.offsets.from = word.offsets.from;
+				nextWord.timestamps.from = word.offsets.from;
+			} else if(word.text && word.text[0] != ' ' && prevWord) {
+				prevWord.offsets.to = word.offsets.to;
+				prevWord.timestamps.to = word.timestamps.to;
+				prevWord.text += word.text;
+				skip = true;
+			}
+			return !!word.text && !skip;
+		}).forEach((word: any) => {
+			const capital = /^[A-Z]/.test(word.text.trim());
+			const length = word.offsets.to - word.offsets.from;
+			const syllables = countSyllables(word.text.trim());
+			const expected = syllables * cadence;
+			if(capital && length > expected * 2 && word.text[0] == ' ') result += '.';
+			result += word.text;
+		});
+		if(!llm) return result.trim();
+		return this.ai.language.ask(result, {
+			system: 'Remove any misplaced punctuation from the following ASR transcript using the replace tool. Avoid modifying words unless there is an obvious typo',
+			temperature: 0.1,
+			tools: [{
+				name: 'replace',
+				description: 'Use find and replace to fix errors',
+				args: {
+					find: {type: 'string', description: 'Text to find', required: true},
+					replace: {type: 'string', description: 'Text to replace', required: true}
+				},
+				fn: (args) => result = result.replace(args.find, args.replace)
+			}]
+		}).then(() => result);
+	}
+
+	private async diarizeTranscript(timestampData: any, speakers: any[], llm: boolean): Promise<string> {
+		const speakerMap = new Map();
+		let speakerCount = 0;
+		speakers.forEach((seg: any) => {
+			if(!speakerMap.has(seg.speaker)) speakerMap.set(seg.speaker, ++speakerCount);
+		});
+
+		const punctuatedText = await this.addPunctuation(timestampData, llm);
+		const sentences = punctuatedText.match(/[^.!?]+[.!?]+/g) || [punctuatedText];
+		const words = timestampData.transcription.filter((w: any) => w.text.trim());
+
+		// Assign speaker to each sentence
+		const sentencesWithSpeakers = sentences.map(sentence => {
+			sentence = sentence.trim();
+			if(!sentence) return null;
+
+			const sentenceWords = sentence.toLowerCase().replace(/[^\w\s]/g, '').split(/\s+/);
+			const speakerWordCount = new Map<number, number>();
+
+			sentenceWords.forEach(sw => {
+				const word = words.find((w: any) => sw === w.text.trim().toLowerCase().replace(/[^\w]/g, ''));
+				if(!word) return;
+
+				const wordTime = word.offsets.from / 1000;
+				const speaker = speakers.find((seg: any) => wordTime >= seg.start && wordTime <= seg.end);
+				if(speaker) {
+					const spkNum = speakerMap.get(speaker.speaker);
+					speakerWordCount.set(spkNum, (speakerWordCount.get(spkNum) || 0) + 1);
+				}
+			});
+
+			let bestSpeaker = 1;
+			let maxWords = 0;
+			speakerWordCount.forEach((count, speaker) => {
+				if(count > maxWords) {
+					maxWords = count;
+					bestSpeaker = speaker;
+				}
+			});
+
+			return {speaker: bestSpeaker, text: sentence};
+		}).filter(s => s !== null);
+
+		// Merge adjacent sentences from same speaker
+		const merged: Array<{speaker: number, text: string}> = [];
+		sentencesWithSpeakers.forEach(item => {
+			const last = merged[merged.length - 1];
+			if(last && last.speaker === item.speaker) {
+				last.text += ' ' + item.text;
+			} else {
+				merged.push({...item});
+			}
+		});
+
+		let transcript = merged.map(item => `[Speaker ${item.speaker}]: ${item.text}`).join('\n').trim();
+		if(!llm) return transcript;
+		let chunks = this.ai.language.chunk(transcript, 500, 0);
+		if(chunks.length > 4) chunks = [...chunks.slice(0, 3), <string>chunks.at(-1)];
+		const names = await this.ai.language.json(chunks.join('\n'), '{1: "Detected Name", 2: "Second Name"}', {
+			system: 'Use the following transcript to identify speakers. Only identify speakers you are positive about, dont mention speakers you are unsure about in your response',
+			temperature: 0.1,
+		});
+		Object.entries(names).forEach(([speaker, name]) => transcript = transcript.replaceAll(`[Speaker ${speaker}]`, `[${name}]`));
+		return transcript;
+	}
+
 	private runAsr(file: string, opts: {model?: string, diarization?: boolean} = {}): AbortablePromise<any> {
 		let proc: any;
 		const p = new Promise<any>((resolve, reject) => {
@@ -111,102 +226,28 @@ print(json.dumps(segments))
 		return <any>Object.assign(p, {abort});
 	}
 
-	private async combineSpeakerTranscript(punctuatedText: string, timestampData: any, speakers: any[]): Promise<string> {
-		const speakerMap = new Map();
-		let speakerCount = 0;
-		speakers.forEach((seg: any) => {
-			if(!speakerMap.has(seg.speaker)) speakerMap.set(seg.speaker, ++speakerCount);
-		});
-
-		const sentences = punctuatedText.match(/[^.!?]+[.!?]+/g) || [punctuatedText];
-		const lines: string[] = [];
-
-		sentences.forEach(sentence => {
-			sentence = sentence.trim();
-			if(!sentence) return;
-
-			const words = sentence.toLowerCase().replace(/[^\w\s]/g, '').split(/\s+/);
-			let startTime = Infinity, endTime = 0;
-			const wordTimings: {start: number, end: number}[] = [];
-
-			timestampData.transcription.forEach((word: any) => {
-				const wordText = word.text.trim().toLowerCase();
-				if(words.some(w => wordText.includes(w))) {
-					const start = word.offsets.from / 1000;
-					const end = word.offsets.to / 1000;
-					wordTimings.push({start, end});
-					if(start < startTime) startTime = start;
-					if(end > endTime) endTime = end;
-				}
-			});
-
-			if(startTime === Infinity) return;
-
-			// Weight by word-level overlap instead of sentence span
-			const speakerScores = new Map<number, number>();
-
-			wordTimings.forEach(wt => {
-				speakers.forEach((seg: any) => {
-					const overlap = Math.max(0, Math.min(wt.end, seg.end) - Math.max(wt.start, seg.start));
-					const duration = wt.end - wt.start;
-					if(duration > 0) {
-						const score = overlap / duration; // % of word covered
-						const spkNum = speakerMap.get(seg.speaker);
-						speakerScores.set(spkNum, (speakerScores.get(spkNum) || 0) + score);
-					}
-				});
-			});
-
-			let bestSpeaker = 1;
-			let maxScore = 0;
-			speakerScores.forEach((score, speaker) => {
-				if(score > maxScore) {
-					maxScore = score;
-					bestSpeaker = speaker;
-				}
-			});
-
-			lines.push(`[Speaker ${bestSpeaker}]: ${sentence}`);
-		});
-
-		return lines.join('\n').trim();
-	}
-
-	asr(file: string, options: { model?: string; diarization?: boolean | 'id' } = {}): AbortablePromise<string | null> {
+	asr(file: string, options: { model?: string; diarization?: boolean | 'llm' } = {}): AbortablePromise<string | null> {
 		if(!this.ai.options.whisper) throw new Error('Whisper not configured');
 
 		const tmp = join(mkdtempSync(join(tmpdir(), 'audio-')), 'converted.wav');
 		execSync(`ffmpeg -i "${file}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
 		const clean = () => fs.rm(Path.dirname(tmp), {recursive: true, force: true}).catch(() => {});
-		const transcript = this.runAsr(tmp, {model: options.model, diarization: false});
-		const timestamps: any = !options.diarization ? Promise.resolve(null) : this.runAsr(tmp, {model: options.model, diarization: true});
-		const diarization: any = !options.diarization ? Promise.resolve(null) : this.runDiarization(tmp);
+
+		if(!options.diarization) return this.runAsr(tmp, {model: options.model});
+		const timestamps = this.runAsr(tmp, {model: options.model, diarization: true});
+		const diarization = this.runDiarization(tmp);
 		let aborted = false, abort = () => {
 			aborted = true;
-			transcript.abort();
-			timestamps?.abort?.();
-			diarization?.abort?.();
+			timestamps.abort();
+			diarization.abort();
 			clean();
 		};
 
-		const response = Promise.allSettled([transcript, timestamps, diarization]).then(async ([t, ts, d]) => {
-			if(t.status == 'rejected') throw new Error('Whisper.cpp punctuated:\n' + t.reason);
+		const response = Promise.allSettled([timestamps, diarization]).then(async ([ts, d]) => {
 			if(ts.status == 'rejected') throw new Error('Whisper.cpp timestamps:\n' + ts.reason);
 			if(d.status == 'rejected') throw new Error('Pyannote:\n' + d.reason);
-			if(aborted || !options.diarization) return t.value;
-
-			let transcript = await this.combineSpeakerTranscript(t.value, ts.value, d.value);
-			if(!aborted && options.diarization === 'id') {
-				if(!this.ai.language.defaultModel) throw new Error('Configure an LLM for advanced ASR speaker detection');
-				let chunks = this.ai.language.chunk(transcript, 500, 0);
-				if(chunks.length > 4) chunks = [...chunks.slice(0, 3), <string>chunks.at(-1)];
-				const names = await this.ai.language.json(chunks.join('\n'), '{1: "Detected Name", 2: "Second Name"}', {
-					system: 'Use the following transcript to identify speakers. Only identify speakers you are positive about, dont mention speakers you are unsure about in your response',
-					temperature: 0.1,
-				});
-				Object.entries(names).forEach(([speaker, name]) => transcript = transcript.replaceAll(`[Speaker ${speaker}]`, `[${name}]`));
-			}
-			return transcript;
+			if(aborted || !options.diarization) return ts.value;
+			return this.diarizeTranscript(ts.value, d.value, options.diarization == 'llm');
 		}).finally(() => clean());
 		return <any>Object.assign(response, {abort});
 	}

src/llm.ts

@@ -44,8 +44,6 @@ export type LLMMemory = {
 	fact: string;
 	/** Owner and fact embedding vector */
 	embeddings: [number[], number[]];
-	/** Creation time */
-	timestamp: Date;
 }
 
 export type LLMRequest = {
@@ -95,75 +93,99 @@ class LLM {
 	 * @returns {{abort: () => void, response: Promise<string>}} Function to abort response and chat history
 	 */
 	ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
+		options = <any>{
+			system: '',
+			temperature: 0.8,
+			...this.ai.options.llm,
+			models: undefined,
+			history: [],
+			...options,
+		}
 		const m = options.model || this.defaultModel;
 		if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
 		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 memories were passed, find any relevant 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 search = async (query?: string | null, subject?: string | null, limit = 10) => {
 					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);
+					return (options.memory || []).map(m => {
+						const score = (o ? this.cosineSimilarity(m.embeddings[0], o[0].embedding) : 0)
+							+ (q ? this.cosineSimilarity(m.embeddings[1], q[0].embedding) : 0);
+						return {...m, score};
+					}).toSorted((a: any, b: any) => a.score - b.score).slice(0, limit)
+						.map(m => `- ${m.owner}: ${m.fact}`).join('\n');
 				}
 
+				options.system += '\nYou have RAG memory and will be given the top_k closest memories regarding the users query. Save anything new you have learned worth remembering from the user message using the remember tool and feel free to recall memories manually.\n';
 				const relevant = await search(message);
-				if(relevant.length) options.history.push({role: 'assistant', content: 'Things I remembered:\n' + relevant.map(m => `${m.owner}: ${m.fact}`).join('\n')});
-				options.tools = [...options.tools || [], {
-					name: 'read_memory',
-					description: 'Check your long-term memory for more information',
+				if(relevant.length) options.history.push({role: 'tool', name: 'recall', id: 'auto_recall_' + Math.random().toString(), args: {}, content: `Things I remembered:\n${relevant}`});
+				options.tools = [{
+					name: 'recall',
+					description: 'Recall the closest memories you have regarding a query using RAG',
 					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'},
+						topK: {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);
+						return search(args.query, args.subject, args.topK);
 					}
-				}];
+				}, {
+					name: 'remember',
+					description: 'Store important facts user shares for future recall',
+					args: {
+						owner: {type: 'string', description: 'Subject/person this fact is about'},
+						fact: {type: 'string', description: 'The information to remember'}
+					},
+					fn: async (args) => {
+						if(!options.memory) return;
+						const e = await Promise.all([
+							this.embedding(args.owner),
+							this.embedding(`${args.owner}: ${args.fact}`)
+						]);
+						const newMem = {owner: args.owner, fact: args.fact, embeddings: <any>[e[0][0].embedding, e[1][0].embedding]};
+						options.memory.splice(0, options.memory.length, ...[
+							...options.memory.filter(m => {
+								return !(this.cosineSimilarity(newMem.embeddings[0], m.embeddings[0]) >= 0.9 && this.cosineSimilarity(newMem.embeddings[1], m.embeddings[1]) >= 0.8);
+							}),
+							newMem
+						]);
+						return 'Remembered!';
+					}
+				}, ...options.tools || []];
 			}
 
 			// 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);
+			// Remove any memory calls from history
+			if(options.memory) options.history.splice(0, options.history.length, ...options.history.filter(h => h.role != 'tool' || (h.name != 'recall' && h.name != 'remember')));
+
+			// Compress message history
+			if(options.compress) {
+				const compressed = await this.ai.language.compressHistory(options.history, options.compress.max, options.compress.min, options);
+				options.history.splice(0, options.history.length, ...compressed);
 			}
 
-			// 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});
 	}
 
+	async code(message: string, options?: LLMRequest): Promise<any> {
+		const resp = await this.ask(message, {...options, system: [
+			options?.system,
+			'Return your response in a code block'
+		].filter(t => !!t).join(('\n'))});
+		const codeBlock = /```(?:.+)?\s*([\s\S]*?)```/.exec(resp);
+		return codeBlock ? codeBlock[1].trim() : null;
+	}
+
 	/**
 	 * Compress chat history to reduce context size
 	 * @param {LLMMessage[]} history Chatlog that will be compressed
@@ -172,32 +194,24 @@ class LLM {
 	 * @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<{history: LLMMessage[], memory: LLMMemory[]}> {
-		if(this.estimateTokens(history) < max) return {history, memory: []};
+	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()) {
 			tokens += this.estimateTokens(m.content);
 			if(tokens < min) keep++;
 			else break;
 		}
-		if(history.length <= keep) return {history, memory: []};
+		if(history.length <= keep) return history;
 		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: any = await this.json(process.map(m => `${m.role}: ${m.content}`).join('\n\n'), '{summary: string, facts: [[subject, fact]]}', {
-			system: 'Create the smallest summary possible, no more than 500 tokens. Create a list of NEW facts (split by subject [pro]noun and fact) about what you learned from this conversation that you didn\'t already know or get from a tool call or system prompt. Focus only on new information about people, topics, or facts. Avoid generating facts about the AI.',
-			model: options?.model,
-			temperature: options?.temperature || 0.3
-		});
-		const timestamp = new Date();
-		const memory = await Promise.all((summary?.facts || [])?.map(async ([owner, fact]: [string, string]) => {
-			const e = await Promise.all([this.embedding(owner), this.embedding(`${owner}: ${fact}`)]);
-			return {owner, fact, embeddings: [e[0][0].embedding, e[1][0].embedding], timestamp};
-		}));
-		const h = [{role: 'assistant', content: `Conversation Summary: ${summary?.summary}`, timestamp: Date.now()}, ...recent];
+		const summary: any = await this.summarize(process.map(m => `[${m.role}]: ${m.content}`).join('\n\n'), 500, options);
+		const d = Date.now();
+		const h = [{role: <any>'tool', name: 'summary', id: `summary_` + d, args: {}, content: `Conversation Summary: ${summary?.summary}`, timestamp: d}, ...recent];
 		if(system) h.splice(0, 0, system);
-		return {history: <any>h, memory};
+		return h;
 	}
 
 	/**
@@ -234,7 +248,7 @@ class LLM {
 				return `${p}: ${Array.isArray(value) ? value.join(', ') : value}`;
 			});
 		};
-		const lines = typeof target === 'object' ? objString(target) : target.split('\n');
+		const lines = typeof target === 'object' ? objString(target) : target.toString().split('\n');
 		const tokens = lines.flatMap(l => [...l.split(/\s+/).filter(Boolean), '\n']);
 		const chunks: string[] = [];
 		for(let i = 0; i < tokens.length;) {
@@ -343,14 +357,11 @@ class LLM {
 	 * @returns {Promise<{} | {} | RegExpExecArray | null>}
 	 */
 	async json(text: string, schema: string, options?: LLMRequest): Promise<any> {
-		let resp = await this.ask(text, {...options, system: (options?.system ? `${options.system}\n` : '') + `Only respond using a JSON code block matching this schema:
-\`\`\`json
-${schema}
-\`\`\``});
-		if(!resp) return {};
-		const codeBlock = /```(?:.+)?\s*([\s\S]*?)```/.exec(resp);
-		const jsonStr = codeBlock ? codeBlock[1].trim() : resp;
-		return JSONAttemptParse(jsonStr, {});
+		const code = await this.code(text, {...options, system: [
+			options?.system,
+			`Only respond using JSON matching this schema:\n\`\`\`json\n${schema}\n\`\`\``
+		].filter(t => !!t).join('\n')});
+		return code ? JSONAttemptParse(code, {}) : null;
 	}
 
 	/**
@@ -360,8 +371,8 @@ ${schema}
 	 * @param options LLM request options
 	 * @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});
+	summarize(text: string, tokens: number = 500, options?: LLMRequest): Promise<string | null> {
+		return this.ask(text, {system: `Generate the shortest summary possible <= ${tokens} tokens. Output nothing else`, temperature: 0.3, ...options});
 	}
 }
 

src/open-ai.ts

@@ -11,7 +11,7 @@ export class OpenAi extends LLMProvider {
 		super();
 		this.client = new openAI(clean({
 			baseURL: host,
-			apiKey: token
+			apiKey: token || host ? 'ignored' : undefined
 		}));
 	}
 
@@ -67,7 +67,10 @@ export class OpenAi extends LLMProvider {
 	ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
 		const controller = new AbortController();
 		return Object.assign(new Promise<any>(async (res, rej) => {
-			if(options.system && options.history?.[0]?.role != 'system') options.history?.splice(0, 0, {role: 'system', content: options.system, timestamp: Date.now()});
+			if(options.system) {
+				if(options.history?.[0]?.role != 'system') options.history?.splice(0, 0, {role: 'system', content: options.system, timestamp: Date.now()});
+				else options.history[0].content = options.system;
+			}
 			let history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
 			const tools = options.tools || this.ai.options.llm?.tools || [];
 			const requestParams: any = {
@@ -100,15 +103,37 @@ export class OpenAi extends LLMProvider {
 				if(options.stream) {
 					if(!isFirstMessage) options.stream({text: '\n\n'});
 					else isFirstMessage = false;
-					resp.choices = [{message: {content: '', tool_calls: []}}];
+					resp.choices = [{message: {role: 'assistant', content: '', tool_calls: []}}];
 					for await (const chunk of resp) {
 						if(controller.signal.aborted) break;
 						if(chunk.choices[0].delta.content) {
 							resp.choices[0].message.content += chunk.choices[0].delta.content;
 							options.stream({text: chunk.choices[0].delta.content});
 						}
+
 						if(chunk.choices[0].delta.tool_calls) {
-							resp.choices[0].message.tool_calls = 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);
+								if(existing) {
+									if(deltaTC.id) existing.id = deltaTC.id;
+									if(deltaTC.type) existing.type = deltaTC.type;
+									if(deltaTC.function) {
+										if(!existing.function) existing.function = {};
+										if(deltaTC.function.name) existing.function.name = deltaTC.function.name;
+										if(deltaTC.function.arguments) existing.function.arguments = (existing.function.arguments || '') + deltaTC.function.arguments;
+									}
+								} else {
+									resp.choices[0].message.tool_calls.push({
+										index: deltaTC.index,
+										id: deltaTC.id || '',
+										type: deltaTC.type || 'function',
+										function: {
+											name: deltaTC.function?.name || '',
+											arguments: deltaTC.function?.arguments || ''
+										}
+									});
+								}
+							}
 						}
 					}
 				}

src/tools.ts

@@ -1,9 +1,15 @@
 import * as cheerio from 'cheerio';
-import {$, $Sync} from '@ztimson/node-utils';
+import {$Sync} from '@ztimson/node-utils';
 import {ASet, consoleInterceptor, Http, fn as Fn} from '@ztimson/utils';
+import * as os from 'node:os';
 import {Ai} from './ai.ts';
 import {LLMRequest} from './llm.ts';
 
+const getShell = () => {
+	if(os.platform() == 'win32') return 'cmd';
+	return $Sync`echo $SHELL`?.split('/').pop() || 'bash';
+}
+
 export type AiToolArg = {[key: string]: {
 	/** Argument type */
 	type: 'array' | 'boolean' | 'number' | 'object' | 'string',
@@ -40,7 +46,7 @@ export const CliTool: AiTool = {
 	name: 'cli',
 	description: 'Use the command line interface, returns any output',
 	args: {command: {type: 'string', description: 'Command to run', required: true}},
-	fn: (args: {command: string}) => $`${args.command}`
+	fn: (args: {command: string}) => $Sync`${args.command}`
 }
 
 export const DateTimeTool: AiTool = {
@@ -54,19 +60,20 @@ export const ExecTool: AiTool = {
 	name: 'exec',
 	description: 'Run code/scripts',
 	args: {
-		language: {type: 'string', description: 'Execution language', enum: ['cli', 'node', 'python'], required: true},
+		language: {type: 'string', description: `Execution language (CLI: ${getShell()})`, enum: ['cli', 'node', 'python'], required: true},
 		code: {type: 'string', description: 'Code to execute', required: true}
 	},
 	fn: async (args, stream, ai) => {
 		try {
-			switch(args.type) {
-				case 'bash':
+			switch(args.language) {
+				case 'cli':
 					return await CliTool.fn({command: args.code}, stream, ai);
 				case 'node':
 					return await JSTool.fn({code: args.code}, stream, ai);
-				case 'python': {
+				case 'python':
 					return await PythonTool.fn({code: args.code}, stream, ai);
-				}
+				default:
+					throw new Error(`Unsupported language: ${args.language}`);
 			}
 		} catch(err: any) {
 			return {error: err?.message || err.toString()};
@@ -98,9 +105,9 @@ export const JSTool: AiTool = {
 		code: {type: 'string', description: 'CommonJS javascript', required: true}
 	},
 	fn: async (args: {code: string}) => {
-		const console = consoleInterceptor(null);
-		const resp = await Fn<any>({console}, args.code, true).catch((err: any) => console.output.error.push(err));
-		return {...console.output, return: resp, stdout: undefined, stderr: undefined};
+		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};
 	}
 }
 

tsconfig.json

@@ -15,6 +15,7 @@
 		"noEmit": true,
 
 		/* Linting */
-		"strict": true
+		"strict": true,
+		"noImplicitAny": false
 	}
 }