diff --git a/README.md b/README.md index a070ee3..330d7fc 100644 --- a/README.md +++ b/README.md @@ -103,7 +103,125 @@ A TypeScript library that provides a unified interface for working with multiple ## Documentation -[Available Here](https://ai-utils.docs.zakscode.com/) +### Setup +```javascript +const ai = new Ai({ + path: '/ai-models', + + // Setup audio + whisper: '/path/to/binary', // Required for ASR + hfToken: '...', // Required for diarization + asr: 'ggml-base.en.bin', // Override default ASR model + + // Setup LLM + embedder: 'bge-small-en-v1.5', // Override default embedder model + llm: { + system: 'You are a helpful assistant.', + compress: {max: 90_000, min: 50_000}, // Compress chat history to min tokens when max is reached + temperature: 0.8, + max_tokens: 100_000, + memoryModel: 'gpt-4o', // Cheap model for managing memories in background, defaults to current model + models: { + 'claude-3-5-sonnet': {proto: 'anthropic', token: process.env.ANTHROPIC_TOKEN}, + 'gpt-4o': {proto: 'openai', token: process.env.OPENAI_TOKEN}, + 'llama3': {proto: 'ollama', host: 'http://localhost:11434'}, + }, + mcp: [ + {name: 'files', url: 'https://mcp.example.com', token: process.env.MCP_TOKEN} + ], + skills: [ + {name: 'Tone of voice', description: 'Brand writing guidelines', content: '# Tone of Voice\n\nAlways be concise and friendly...'} + ], + tools: [{ + name: 'Marco?', + description: 'Where is marco polo?', + args: { + shout: {type: 'boolean', default: 'Shout into the void?', description: false, required: false} + }, + fn: (args: any, stream: LLMRequest['stream'], ai: Ai) => { + const {shout} = args; + return shout ? 'Polo!' : 'Polo'; + } + }], + }, + + // Setup Vision + ocr: 'eng' // Override default OCR model +}); + +``` + +### Audio + +```javascript +// Crate audio transcript +const text = await ai.audio.asr('./path/to/audio.mp3'); +console.log(text); + +// Break transcript into speakers +const text = await ai.audio.asr('./path/to/audio.mp3', {diarization: true}); +console.log(text); + +// Break transcript into named speakers +const text = await ai.audio.asr('./path/to/audio.mp3', {diarization: 'llm'}); +console.log(text); +``` + +### Language + +```javascript +const history = [], memory = []; + +// Wait for entire response +const text = await ai.language.ask('My favorite color is blue, whats yours?', {history, memory}); +console.log(text); + +// Stream response +const chunks = ''; +await ai.language.ask('Write me a poem', { + history, memory, + stream: chunk => chunks += chunk, +}); +console.log(chunks); + +// Manually compile history into memories at end of conversation +// Happens automatically when coverstaions are compressed +await ai.language.updateMemory(history, memory); + +// Summarize text +const summary = await ai.language.summarize(longText, 200); + +// Code response (no conversation or extra BS) +const code = await ai.language.code('Write a fibonacci function'); + +// Structured JSON response +const data = await ai.language.json('Extract the name and age', `{ + "name": "string", + "age": "number" +}`, {system: 'Extract from user input'}); +``` + +#### Premade LLM Tools: +- `cli`: Run a shell command, returns its output +- `get_datetime`: Returns local date/time +- `get_datetime_utc`: Returns current UTC date/time +- `exec`: Execute code in cli, node, or python +- `fetch`: Make HTTP requests (GET/POST/PUT/DELETE) +- `exec_javascript`: Execute CommonJS JavaScript +- `exec_python`: Execute Python via python -c +- `read_webpage`: Scrape & clean content from a URL, handles HTML, JSON, CSV, media, PDFs etc. +- `web_search`: Anonymous DuckDuckGo search, returns a list of URLs +- `wikipedia_lookup`: Fetch a Wikipedia article (intro or full) +- `wikipedia_search`: Search Wikipedia and return matching articles +- `get_weather`: Fetch current weather + forecast for a location (just built!) + +### Vision + +```javascript +// Extract text from image +const text = await ai.vision.ocr('./path/to/image.png'); +console.log(text); +``` ## License diff --git a/main.mjs b/main.mjs new file mode 100644 index 0000000..1403f9e --- /dev/null +++ b/main.mjs @@ -0,0 +1,21 @@ +import {Ai} from './dist/index.mjs'; + +const ai = new Ai({ + path: './', + llm: { + system: 'You are a testbed for developing an AI library', + models: { + 'qwen/qwen3.5-9b': {proto: 'openai', host: 'http://127.0.0.1:1234/v1'} + } + } +}); + +const skills = [{ + name: 'Momentum', + description: 'Learn how to use the Momentum API', + content: 'You can initialize it with: new Momentum(url);' +}]; + +const history = [], memory = []; +console.log(await ai.language.ask('Can you tell me how to use momentum?', {history, skills})); +console.log(history, memory); diff --git a/package-lock.json b/package-lock.json index 3ddf4ac..52d20a9 100644 --- a/package-lock.json +++ b/package-lock.json @@ -1,38 +1,39 @@ { "name": "@ztimson/ai-utils", - "version": "0.8.16", + "version": "1.0.0", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "@ztimson/ai-utils", - "version": "0.8.16", + "version": "1.0.0", "license": "MIT", "dependencies": { - "@anthropic-ai/sdk": "^0.78.0", + "@anthropic-ai/sdk": "^0.102.0", + "@huggingface/transformers": "^4.2.0", "@tensorflow/tfjs": "^4.22.0", - "@xenova/transformers": "^2.17.2", "@ztimson/node-utils": "^1.0.7", - "@ztimson/utils": "^0.28.16", + "@ztimson/utils": "^0.29.4", "cheerio": "^1.2.0", - "openai": "^6.22.0", + "openai": "^6.42.0", "tesseract.js": "^7.0.0" }, "devDependencies": { - "@types/node": "^24.8.1", + "@types/node": "^24.13.1", "typedoc": "^0.26.7", - "typescript": "^5.3.3", - "vite": "^7.2.7", - 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"version": "0.9.0", + "version": "1.0.0", "description": "AI Utility library", "author": "Zak Timson", "license": "MIT", @@ -25,21 +25,21 @@ "watch": "npx vite build --watch" }, "dependencies": { - "@anthropic-ai/sdk": "^0.78.0", + "@anthropic-ai/sdk": "^0.102.0", "@tensorflow/tfjs": "^4.22.0", - "@xenova/transformers": "^2.17.2", + "@huggingface/transformers": "^4.2.0", "@ztimson/node-utils": "^1.0.7", - "@ztimson/utils": "^0.28.16", + "@ztimson/utils": "^0.29.4", "cheerio": "^1.2.0", - "openai": "^6.22.0", + "openai": "^6.42.0", "tesseract.js": "^7.0.0" }, "devDependencies": { - "@types/node": "^24.8.1", + "@types/node": "^24.13.1", "typedoc": "^0.26.7", - "typescript": "^5.3.3", - "vite": "^7.2.7", - "vite-plugin-dts": "^4.5.3" + "typescript": "^5.6.3", + "vite": "^8.0.16", + "vite-plugin-dts": "^5.0.2" }, "files": [ "dist" diff --git a/src/ai.ts b/src/ai.ts index bb6fa76..f325734 100644 --- a/src/ai.ts +++ b/src/ai.ts @@ -8,7 +8,7 @@ export type AbortablePromise = Promise & { }; export type AiOptions = { - /** Token to pull models from hugging face */ + /** Token to pull diarization models from hugging face */ hfToken?: string; /** Path to models */ path?: string; diff --git a/src/audio.ts b/src/audio.ts index 3301fca..8efdb97 100644 --- a/src/audio.ts +++ b/src/audio.ts @@ -2,7 +2,6 @@ import {execSync, spawn} from 'node:child_process'; import {mkdtempSync} from 'node:fs'; import fs from 'node:fs/promises'; import {tmpdir} from 'node:os'; -import * as path from 'node:path'; import Path, {join} from 'node:path'; import {AbortablePromise, Ai} from './ai.ts'; @@ -155,7 +154,7 @@ print(json.dumps(segments)) const p = new Promise((resolve, reject) => { this.downloadAsrModel(opts.model).then(m => { if(opts.diarization) { - let output = path.join(path.dirname(file), 'transcript'); + let output = join(Path.dirname(file), 'transcript'); proc = spawn(this.ai.options.whisper, ['-m', m, '-f', file, '-np', '-ml', '1', '-oj', '-of', output], {stdio: ['ignore', 'ignore', 'pipe']} @@ -226,11 +225,11 @@ print(json.dumps(segments)) return Object.assign(p, {abort}); } - asr(file: string, options: { model?: string; diarization?: boolean | 'llm' } = {}): AbortablePromise { + asr(path: string, options: { model?: string; diarization?: boolean | 'llm' } = {}): AbortablePromise { 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' }); + execSync(`ffmpeg -i "${path}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' }); const clean = () => fs.rm(Path.dirname(tmp), {recursive: true, force: true}).catch(() => {}); if(!options.diarization) return this.runAsr(tmp, {model: options.model}); diff --git a/src/embedder.ts b/src/embedder.ts index 8b0d0ba..d232acd 100644 --- a/src/embedder.ts +++ b/src/embedder.ts @@ -1,13 +1,13 @@ -import { pipeline } from '@xenova/transformers'; +import { pipeline } from '@huggingface/transformers'; const [modelDir, model] = process.argv.slice(2); let text = ''; process.stdin.on('data', chunk => text += chunk); process.stdin.on('end', async () => { - const embedder = await pipeline('feature-extraction', 'Xenova/' + model, {quantized: true, cache_dir: modelDir}); + const embedder = await pipeline('feature-extraction', 'Xenova/' + model, {cache_dir: modelDir}); const output = await embedder(text, { pooling: 'mean', normalize: true }); const embedding = Array.from(output.data); - console.log(JSON.stringify({embedding})); + process.stdout.write(JSON.stringify({embedding})); process.exit(); }); diff --git a/src/llm.ts b/src/llm.ts index 8a1f20c..eced138 100644 --- a/src/llm.ts +++ b/src/llm.ts @@ -1,5 +1,3 @@ -import {sum} from '@tensorflow/tfjs'; -import {JSONAttemptParse} from '@ztimson/utils'; import {AbortablePromise, Ai} from './ai.ts'; import {Anthropic} from './antrhopic.ts'; import {OpenAi} from './open-ai.ts'; @@ -7,7 +5,8 @@ import {LLMProvider} from './provider.ts'; import {AiTool} from './tools.ts'; import {fileURLToPath} from 'url'; import {dirname, join} from 'path'; -import { spawn } from 'node:child_process'; +import {spawn} from 'node:child_process'; +import {Memory, MemoryManager} from './memory.ts'; export type AnthropicConfig = {proto: 'anthropic', token: string}; export type OllamaConfig = {proto: 'ollama', host: string}; @@ -37,16 +36,6 @@ export type LLMMessage = { 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[]]; -} - export type LLMRequest = { /** System prompt */ system?: string; @@ -63,17 +52,39 @@ export type LLMRequest = { /** Stream response */ stream?: (chunk: {text?: string, tool?: string, done?: true}) => any; /** Compress old messages in the chat to free up context */ - compress?: { - /** Trigger chat compression once context exceeds the token count */ - max: number; - /** Compress chat until context size smaller than */ - min: number - }, - /** Background information the AI will be fed */ - memory?: LLMMemory[], + compress?: {max: number; min: number}; + /** User's memory documents - RAG injected automatically each turn */ + memory?: Memory[]; + /** Model to use for memory operations */ + memoryModel?: string; + /** Skill documents the AI can browse and read on demand */ + skills?: Skill[]; + /** MCP servers to connect and expose as tools */ + mcp?: McpServer[]; } +export type McpServer = { + /** MCP server name for humans */ + name: string; + /** Host URL */ + host: string; + /** Server access token */ + token?: string; +} + +export type Skill = { + /** Name of skill for humans */ + name: string; + /** Description LLM will use to decide to learn a skill */ + description: string; + /** Skill instructions */ + content: string; +} + + class LLM { + private memoryManager!: MemoryManager; + defaultModel!: string; models: {[model: string]: LLMProvider} = {}; @@ -85,14 +96,67 @@ class LLM { else if(config.proto == 'ollama') this.models[model] = new OpenAi(this.ai, config.host, 'not-needed', model); else if(config.proto == 'openai') this.models[model] = new OpenAi(this.ai, config.host || null, config.token, model); }); + this.memoryManager = new MemoryManager(this); + } + + private async setupMcp(servers: McpServer[] = []): Promise<{prompt: string, tools: AiTool[]}> { + if(!servers?.length) return {prompt: '', tools: []}; + const allTools: AiTool[] = []; + await Promise.all(servers.map(async server => { + const res = await fetch(`${server.host}/tools`, {headers: server.token ? {Authorization: `Bearer ${server.token}`} : {}}); + const mcp: any = await res.json(); + if(!mcp?.tools) return; + for(const t of mcp.tools) { + const args: Record = {}; + if(t.inputSchema?.properties) { + for(const [key, val] of Object.entries(t.inputSchema.properties)) { + args[key] = {type: val.type || 'string', description: val.description || '', required: t.inputSchema.required?.includes(key)}; + } + } + allTools.push({ + name: `${server.name}_${t.name}`, + description: t.description || '', + args, + fn: async (a: any) => { + const r = await fetch(`${server.host}/tools/call`, { + method: 'POST', + headers: {'Content-Type': 'application/json', ...(server.token ? {Authorization: `Bearer ${server.token}`} : {})}, + body: JSON.stringify({name: t.name, arguments: a}) + }); + const data: any = await r.json(); + return data?.content?.[0]?.text ?? JSON.stringify(data); + } + }); + } + })); + + const list = allTools.map(t => `- ${t.name}: ${t.description}`).join('\n'); + return { + prompt: `You have access to the following MCP tools:\n${list}`, + tools: allTools + }; + } + + private setupSkills(skills: Skill[] = []): {prompt: string, tools: AiTool[]} { + if(!skills?.length) return {prompt: '', tools: []}; + const list = skills.map(s => `- ${s.name}: ${s.description}`).join('\n'); + return { + prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`, + tools: [{ + name: 'read_skill', + description: 'Read the full content of a skill/knowledge document', + args: { + name: {type: 'string', description: 'Exact skill name', required: true} + }, + fn: (args: any) => { + const skill = skills.find(s => s.name === args.name); + if(!skill) return `Skill not found. Available:\n${list}`; + return `# ${skill.name}\n${skill.content}`; + } + }] + } } - /** - * Chat with LLM - * @param {string} message Question - * @param {LLMRequest} options Configuration options and chat history - * @returns {{abort: () => void, response: Promise}} Function to abort response and chat history - */ ask(message: string, options: LLMRequest = {}): AbortablePromise { options = { system: '', @@ -106,71 +170,51 @@ class LLM { if(!this.models[m]) throw new Error(`Model does not exist: ${m}`); let abort = () => {}; return Object.assign(new Promise(async res => { + let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || []; + const prompts: string[] = [options.system || this.ai.options.llm?.system || '']; if(!options.history) options.history = []; - // If memories were passed, find any relevant ones and add a tool for ADHOC lookups - if(options.memory) { - 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 => { - 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}: ${m.fact}`).join('\n'); - } - options.system += '\nYou have RAG memory and will be given the top_k closest memories regarding the users query. Save anything new you have learned worth remembering from the user message using the remember tool and feel free to recall memories manually.\n'; - const relevant = await search(message); - if(relevant.length) options.history.push({role: 'tool', name: 'recall', id: 'auto_recall_' + Math.random().toString(), args: {}, content: `Things I remembered:\n${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'}, - 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.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: [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 || []]; + // MCP + const mcp = options.mcp || this.ai.options?.llm?.mcp; + if(mcp?.length) { + const m = await this.setupMcp(mcp); + prompts.unshift(m.prompt); + tools.push(...m.tools); } - // Ask - const resp = await this.models[m].ask(message, options); + // Skills + const skills = options.skills || this.ai.options?.llm?.skills; + if(skills?.length) { + const s = this.setupSkills(skills); + prompts.unshift(s.prompt); + tools.push(...s.tools); + } - // 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'))); + // Memory + if(options.memory) { + const relevant = await this.memoryManager.recollect(message, options.memory); + if(relevant.length) { + const context = relevant.map(m => `### ${m.name}\n${m.content}`).join('\n\n'); + options.history.push({ + id: 'auto_recall_' + Math.random().toString(), role: 'tool', name: 'recall', args: {}, + content: `Knowledge Documents:\n\n${context}` + }); + } + prompts.unshift('You have access to a knowledge base. Relevant documents are injected automatically before each message. Use this knowledge to inform your responses.'); + } - // Compress message history + const resp = await this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')}); + + // Trim memory injections from history + if(options.memory) { + options.history.splice(0, options.history.length, ...options.history.filter(h => + h.role !== 'tool' || h.name !== 'recall')); + } + + // Auto-memorize before compressing if(options.compress) { - const compressed = await this.ai.language.compressHistory(options.history, options.compress.max, options.compress.min, options); + if(options.memory) await this.memoryManager.memorize(options.history, options.memory, options); + const compressed = await this.compressHistory(options.history, options.compress.max, options.compress.min, options); options.history.splice(0, options.history.length, ...compressed); } @@ -178,13 +222,12 @@ class LLM { }), {abort}); } - async code(message: string, options?: LLMRequest): Promise { - 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; + /** + * Digest full conversation history into memory documents. + * Call on session end to persist the conversation. + */ + async updateMemory(history: LLMMessage[], memories: Memory[], options: LLMRequest = {}): Promise { + await this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options}); } /** @@ -273,7 +316,7 @@ class LLM { * @param {maxTokens?: number, overlapTokens?: number} opts Options for embedding such as chunk sizes * @returns {Promise[]>} Chunked embeddings */ - embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}): AbortablePromise { + embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}): AbortablePromise<{index: number, embedding: number[], text: string, tokens: number}[]> { let {maxTokens = 500, overlapTokens = 50} = opts; let aborted = false; const abort = () => { aborted = true; }; @@ -281,7 +324,6 @@ class LLM { const embed = (text: string): Promise => { return new Promise((resolve, reject) => { if(aborted) return reject(new Error('Aborted')); - const args: string[] = [ join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'), this.ai.options.path, @@ -290,7 +332,6 @@ class LLM { const proc = spawn('node', args, {stdio: ['pipe', 'pipe', 'ignore']}); proc.stdin.write(text); proc.stdin.end(); - let output = ''; proc.stdout.on('data', (data: Buffer) => output += data.toString()); proc.on('close', (code: number) => { @@ -300,7 +341,7 @@ class LLM { const result = JSON.parse(output); resolve(result.embedding); } catch(err) { - reject(new Error('Failed to parse embedding output')); + reject(err); } } else { reject(new Error(`Embedder process exited with code ${code}`)); @@ -320,7 +361,7 @@ class LLM { } return results; })(); - return Object.assign(p, { abort }); + return Object.assign(p, {abort}); } /** @@ -346,8 +387,8 @@ class LLM { (char.charCodeAt(0) * (index + 1)) % dimensions / dimensions).slice(0, dimensions); } const v = vector(target); - const similarities = searchTerms.map(t => vector(t)).map(refVector => this.cosineSimilarity(v, refVector)) - return {avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length, max: Math.max(...similarities), similarities} + const similarities = searchTerms.map(t => vector(t)).map(refVector => this.cosineSimilarity(v, refVector)); + return {avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length, max: Math.max(...similarities), similarities}; } /** @@ -358,7 +399,7 @@ class LLM { * @returns {Promise<{} | {} | RegExpExecArray | null>} */ async json(text: string, schema: string, options?: LLMRequest): Promise { - let system = `Your job is to convert input to JSON using tool calls. Call the \`submit\` tool at least once with JSON matching this schema:\n\`\`\`json\n${schema}\n\`\`\`\n\nResponses are ignored`; + let system = `Your job is to convert input to JSON using tool calls. Call the \`submit\` tool at least once with JSON matching this schema:\n\`\`\`json\n${schema}\n\`\`\`\n\nResponses are ignored`; if(options?.system) system += '\n\n' + options.system; return new Promise(async (resolve, reject) => { let done = false; @@ -392,7 +433,7 @@ class LLM { * @returns {Promise} Summary */ async summarize(text: string, length: number = 500, options?: LLMRequest): Promise { - let system = `Your job is to summarize the users message using tool calls. Call the \`submit\` tool at least once with the shortest summary possible that's <= ${length} words. The tool call will respond with the token count. Responses are ignored`; + let system = `Your job is to summarize the users message using tool calls. Call the \`submit\` tool at least once with the shortest summary possible that's <= ${length} words. The tool call will respond with the token count. Responses are ignored`; if(options?.system) system += '\n\n' + options.system; return new Promise(async (resolve, reject) => { let done = false; diff --git a/src/memory.ts b/src/memory.ts new file mode 100644 index 0000000..5669dbf --- /dev/null +++ b/src/memory.ts @@ -0,0 +1,177 @@ +// memory.ts +import {LLMRequest, LLMMessage} from './llm.ts'; + +/** Background information the AI will be fed as a knowledge document */ +export type Memory = { + /** Memory subject */ + name: string; + /** Short description of what this document contains - used for RAG retrieval */ + description: string; + /** Full markdown content of the document */ + content: string; + /** Embedding vector of the description - used for similarity search */ + embedding: number[]; +} + +export type MemoryCollection = { + /** Memory subject */ + name: string; + /** Short description - required if isNew */ + description?: string; + /** Extracted facts to merge */ + facts: string[]; +} + +export class MemoryManager { + + tools = { + edit: (memory: Memory) => ({ + name: 'edit', + description: 'Edit a memory. Omit start/end to append. Pass start only to replace from that line on. Pass start+end to replace a specific range. start=0 replaces the whole 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 `Updated memory:\n${memory.content}`; + } + }), + extract: (pools: MemoryCollection[]) => ({ + name: 'extract', + 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, only required if new'}, + 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[]) => ({ + name: 'read', + description: 'Read entire memory', + args: { + name: {type: 'string', description: 'Exact memory name', required: true}, + }, + fn: (args: any) => { + const mem = memories.find(m => m.name === args.name); + if(!mem) return 'Document not found'; + return `Name: ${mem.name}\nDescription: ${mem.description}\n\n${mem.content}`; + } + }), + } + + constructor(private llm: any, private model?: string) {} + + /** + * 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} Fact pools grouped by target document + */ + private async extract(conversation: string, memories: Memory[], options: LLMRequest): Promise { + const existingDocs = memories.map(m => `Name: ${m.name}\nDescription: ${m.description}`).join('\n\n'); + const pools: MemoryCollection[] = []; + await this.llm.ask(conversation, { + model: this.model || options.model, + temperature: 0.2, + system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long term. +Rules: +- ONLY extract facts the USER explicitly stated about themselves or their business +- ONLY extract decisions that were MADE during this conversation +- DO NOT extract anything the AI said, its name, capabilities, or how it introduced itself +- DO NOT extract greetings, pleasantries or generic exchanges +- If nothing worth remembering was said, call NO tools + +For each fact decide whether it belongs in an existing document or needs a new one, then call the \`extract\` tool. + +Existing documents:\n${existingDocs || 'None yet.'}`, + tools: [this.tools.extract(pools)] + }); + return pools; + } + + /** + * Bot 2 - Editor: merges a pool of facts into a specific document using surgical line-based edits. + * Receives full document content and uses read + amend tools to make precise edits. + * @param {MemoryCollection} newMem The fact pool to merge + * @param {Memory[]} memories The user's memory documents + * @param {LLMRequest} options LLM options + */ + private async edit(newMem: MemoryCollection, memories: Memory[], options: LLMRequest): Promise { + const existing = memories.find(m => m.name === newMem.name); + const mem: Memory = existing || {name: newMem.name, description: newMem.description || '', content: '', embedding: []}; + const isNew = !existing; + + await this.llm.ask(newMem.facts.map(f => `- ${f}`).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\` tool; call it as many times as necessary. + +Name: ${mem.name} +Description: ${mem.description} +${mem.content}`, + tools: [this.tools.edit(mem)] + } + ); + + if(isNew || mem.description !== existing?.description) { + const [e] = await this.llm.embedding(mem.description); + mem.embedding = e.embedding; + } + + if(isNew) memories.push(mem); + else { + 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} The most relevant memory documents + */ + async recollect(query: string, memories: Memory[], limit = 5): Promise { + const [e] = await this.llm.embedding(query); + return memories + .filter(m => m.embedding?.length) + .map(m => ({...m, score: this.llm.cosineSimilarity(m.embedding, e.embedding)})) + .toSorted((a: any, b: any) => b.score - a.score) + .slice(0, limit); + } + + /** + * Two-stage memory pipeline: classify facts from conversation history then surgically merge them into documents. + * Bot 1 (classify) extracts and groups facts cheaply. Bot 2 (edit) runs per-document in parallel with full content access. + * @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 { + const conversation = history + .filter(h => h.role === 'user' || h.role === 'assistant') + .map(h => `[${h.role}]: ${h.content}`) + .join('\n\n'); + if(!conversation.trim()) return; + const pools = await this.extract(conversation, memories, options); + if(!pools.length) return; + await Promise.all(pools.map(pool => this.edit(pool, memories, options))); + } +} diff --git a/src/open-ai.ts b/src/open-ai.ts index 81d9e58..044966b 100644 --- a/src/open-ai.ts +++ b/src/open-ai.ts @@ -138,6 +138,7 @@ export class OpenAi extends LLMProvider { } } + if(resp.error) throw new Error(resp.error); const toolCalls = resp.choices[0].message.tool_calls || []; if(toolCalls.length && !controller.signal.aborted) { history.push(resp.choices[0].message); @@ -157,7 +158,7 @@ export class OpenAi extends LLMProvider { requestParams.messages = history; } } while (!controller.signal.aborted && resp.choices?.[0]?.message?.tool_calls?.length); - history.push({role: 'assistant', content: resp.choices[0].message.content || ''}); + history.push({role: 'assistant', content: resp.choices[0].message.content.trim() || ''}); history = this.toStandard(history); if(options.stream) options.stream({done: true}); diff --git a/src/vision.ts b/src/vision.ts index 28e2d23..42ff563 100644 --- a/src/vision.ts +++ b/src/vision.ts @@ -17,7 +17,7 @@ export class Vision { const {data} = await worker.recognize(path); await worker.terminate(); res(data.text.trim() || null); - }); + }).finally(() => worker?.terminate()); return Object.assign(p, {abort: () => worker?.terminate()}); } }