Added official file support

README.md

@@ -119,7 +119,7 @@ const ai = new Ai({
         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,
+        maxTokens: 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},

package-lock.json

@@ -1,12 +1,12 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.2.6",
+	"version": "1.5.0",
 	"lockfileVersion": 3,
 	"requires": true,
 	"packages": {
 		"": {
 			"name": "@ztimson/ai-utils",
-			"version": "1.2.6",
+			"version": "1.5.0",
 			"license": "MIT",
 			"dependencies": {
 				"@anthropic-ai/sdk": "^0.102.0",
@@ -16,6 +16,7 @@
 				"@ztimson/utils": "^0.29.4",
 				"cheerio": "^1.2.0",
 				"openai": "^6.42.0",
+				"pdf-parse": "^2.4.5",
 				"tesseract.js": "^7.0.0"
 			},
 			"devDependencies": {
@@ -56,39 +57,12 @@
 				"node": ">=6.9.0"
 			}
 		},
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-			"license": "MIT",
-			"optional": true,
-			"peer": true,
-			"dependencies": {
-				"@emnapi/wasi-threads": "2.0.1",
-				"tslib": "^2.4.0"
-			}
-		},
 		"node_modules/@emnapi/runtime": {
-			"version": "2.0.0-alpha.3",
+			"version": "1.11.3",
-			"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-2.0.0-alpha.3.tgz",
+			"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.3.tgz",
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@@ -577,16 +551,6 @@
 				"url": "https://opencollective.com/libvips"
 			}
 		},
-		"node_modules/@img/sharp-wasm32/node_modules/@emnapi/runtime": {
-			"version": "1.11.3",
-			"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.3.tgz",
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-			"license": "MIT",
-			"optional": true,
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-				"tslib": "^2.4.0"
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 		"node_modules/@img/sharp-win32-arm64": {
 			"version": "0.34.5",
 			"resolved": "https://registry.npmjs.org/@img/sharp-win32-arm64/-/sharp-win32-arm64-0.34.5.tgz",
@@ -694,32 +658,209 @@
 				"@jridgewell/sourcemap-codec": "^1.4.14"
 			}
 		},
-		"node_modules/@napi-rs/wasm-runtime": {
+		"node_modules/@napi-rs/canvas": {
-			"version": "1.2.0",
+			"version": "0.1.80",
-			"resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.2.0.tgz",
+			"resolved": "https://registry.npmjs.org/@napi-rs/canvas/-/canvas-0.1.80.tgz",
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-			"dev": true,
+			"license": "MIT",
+			"workspaces": [
+				"e2e/*"
+			],
+			"engines": {
+				"node": ">= 10"
+			},
+			"optionalDependencies": {
+				"@napi-rs/canvas-android-arm64": "0.1.80",
+				"@napi-rs/canvas-darwin-arm64": "0.1.80",
+				"@napi-rs/canvas-darwin-x64": "0.1.80",
+				"@napi-rs/canvas-linux-arm-gnueabihf": "0.1.80",
+				"@napi-rs/canvas-linux-arm64-gnu": "0.1.80",
+				"@napi-rs/canvas-linux-arm64-musl": "0.1.80",
+				"@napi-rs/canvas-linux-riscv64-gnu": "0.1.80",
+				"@napi-rs/canvas-linux-x64-gnu": "0.1.80",
+				"@napi-rs/canvas-linux-x64-musl": "0.1.80",
+				"@napi-rs/canvas-win32-x64-msvc": "0.1.80"
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 		},
 		"node_modules/@oxc-project/types": {
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@@ -784,9 +925,9 @@
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@@ -801,9 +942,9 @@
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@@ -818,9 +959,9 @@
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@@ -835,9 +976,9 @@
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@@ -869,9 +1010,9 @@
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package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.4.5",
+	"version": "1.6.1",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",
@@ -26,12 +26,13 @@
 	},
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 		"@anthropic-ai/sdk": "^0.102.0",
-		"@tensorflow/tfjs": "^4.22.0",
 		"@huggingface/transformers": "^4.2.0",
+		"@tensorflow/tfjs": "^4.22.0",
 		"@ztimson/node-utils": "^1.0.7",
 		"@ztimson/utils": "^0.29.4",
 		"cheerio": "^1.2.0",
 		"openai": "^6.42.0",
+		"pdf-parse": "^2.4.5",
 		"tesseract.js": "^7.0.0"
 	},
 	"devDependencies": {

src/antrhopic.ts

@@ -24,6 +24,13 @@ export class Anthropic extends LLMProvider {
 		return client;
 	}
 
+	private toWireContent(content: any): any {
+		if(!Array.isArray(content)) return content;
+		return content.map(c => c.type === 'image'
+			? {type: 'image', source: {type: 'base64', media_type: c.mime, data: c.data}}
+			: {type: 'text', text: c.text});
+	}
+
 	/** Convert standard history -> Anthropic wire format */
 	private toWire(history: LLMMessage[]): any[] {
 		const wire: any[] = [];
@@ -34,7 +41,7 @@ export class Anthropic extends LLMProvider {
 					{role: 'user', content: [{type: 'tool_result', tool_use_id: h.id, is_error: !!h.error, content: h.error || h.content || ''}]}
 				);
 			} else {
-				wire.push({role: h.role, content: h.content});
+				wire.push({role: h.role, content: this.toWireContent(h.content)});
 			}
 		}
 		return wire;
@@ -50,7 +57,7 @@ export class Anthropic extends LLMProvider {
 			const tools = options.tools || this.ai.options.llm?.tools || [];
 			const requestParams: any = {
 				model: options.model || this.model,
-				max_tokens: options.max_tokens || this.ai.options.llm?.max_tokens || 4096,
+				max_tokens: options.maxTokens || this.ai.options.llm?.maxTokens || 4096,
 				system: options.system || this.ai.options.llm?.system || '',
 				temperature: options.temperature || this.ai.options.llm?.temperature || undefined,
 				tools: tools.map(t => ({

src/llm.ts

@@ -1,15 +1,20 @@
-import {clean, snakeCase} from '@ztimson/utils';
+import {clean, makeUnique, snakeCase} from '@ztimson/utils';
 import {AbortablePromise, Ai} from './ai.ts';
 import {Anthropic} from './antrhopic.ts';
 import {OpenAi} from './open-ai.ts';
 import {LLMProvider} from './provider.ts';
 import {AiTool, AiToolArg} from './tools.ts';
 import {fileURLToPath} from 'url';
-import {dirname, join} from 'path';
 import {spawn} from 'node:child_process';
-import {Memory, MemoryCache, MemoryManager, MemoryOptions} from './memory.ts';
+import {Memory, MemoryCache, MemoryManager, MemoryOptions, stripHeader} from './memory.ts';
+import {mkdtempSync} from 'node:fs';
+import fs from 'node:fs/promises';
+import {tmpdir} from 'node:os';
+import {dirname, join, basename, extname} from 'path';
+import { PDFParse } from 'pdf-parse';
 
 const MAX_AGENT_DEPTH = 5;
+const PDF_OCR_PAGE_THRESHOLD = 12; // above this many pages, OCR scanned pages instead of feeding images to the model
 
 export type AnthropicConfig = {proto: 'anthropic', token: string | string[]};
 export type OpenAiConfig = {proto: 'openai', host?: string, token: string | string[]};
@@ -27,11 +32,26 @@ export type Agent = {
 	agents?: string[] | null;
 }
 
+export type LLMFile = {
+	/** Path to file on disk */
+	path?: string;
+	/** File content: raw text, base64-encoded binary, or a Buffer */
+	content?: string | Buffer;
+	/** Original filename, used to infer type from extension */
+	name?: string;
+	/** Mime type override, inferred from extension if omitted */
+	mime?: string;
+	/** @internal set once extraction has run, skips re-processing next turn */
+	extracted?: boolean;
+};
+
 export type LLMMessage = {
 	/** Message originator */
 	role: 'assistant' | 'system' | 'user';
 	/** Message content */
 	content: string | any;
+	/** Files attached to request */
+	files?: LLMFile[];
 	/** Timestamp */
 	timestamp?: number;
 	/** Response duration in ms */
@@ -67,7 +87,7 @@ export type LLMRequest = {
 	/** Message history */
 	history?: LLMMessage[];
 	/** Max tokens for request */
-	max_tokens?: number;
+	maxTokens?: number;
 	/** 0 = Rigid Logic, 1 = Balanced, 2 = Hyper Creative **/
 	temperature?: number;
 	/** Available tools */
@@ -88,6 +108,8 @@ export type LLMRequest = {
 	mcp?: McpServer[];
 	/** Subagents exposed as delegatable/wrapped tools */
 	agents?: Agent[];
+	/** Attach files to request */
+	files?: LLMFile[];
 	/** @internal recursion guard for nested agent delegation */
 	_agentDepth?: number;
 }
@@ -111,6 +133,11 @@ export type Skill = {
 }
 
 class LLM {
+	private static AUDIO_EXT = ['wav','mp3','m4a','flac','ogg','aac','wma'];
+	private static IMAGE_EXT = ['png','jpg','jpeg','bmp','gif','tiff','webp'];
+	private static TEXT_EXT = ['txt','md','csv','json','xml','html','js','ts','py','yaml','yml','log'];
+	private static PDF_EXT = ['pdf'];
+
 	private memoryManager!: MemoryManager;
 
 	defaultModel!: string;
@@ -126,6 +153,120 @@ class LLM {
 		this.memoryManager = new MemoryManager(this);
 	}
 
+	private async loadBuffer(file: LLMFile, asText: boolean): Promise<Buffer> {
+		if(file.path) return fs.readFile(file.path);
+		if(Buffer.isBuffer(file.content)) return file.content;
+		if(typeof file.content === 'string') return Buffer.from(file.content, asText ? 'utf-8' : 'base64');
+		throw new Error('No path or content provided');
+	}
+
+	private async writeTemp(name: string, buffer: Buffer): Promise<string> {
+		const path = join(mkdtempSync(join(tmpdir(), 'ai-file-')), name);
+		await fs.writeFile(path, buffer);
+		return path;
+	}
+
+	/**
+	 * Extract text from a PDF. Pages with no text layer (scanned/image-only) are handled as either:
+	 * - Rendered to images and returned alongside the text so the (vision-capable) model can read them directly
+	 * - OCR'd via Tesseract when the doc is too large to reasonably pass as images
+	 */
+	private async resolvePdf(buffer: Buffer): Promise<{text: string, images: {mime: string, data: string}[]}> {
+		const parser = new PDFParse({data: buffer});
+		try {
+			const {text, pages} = await parser.getText();
+			const scanned = (pages || []).filter(p => !p.text?.trim());
+			if(!scanned.length) return {text: text.trim() || '[Empty PDF]', images: []};
+			const total = pages.length;
+			const pageNums = scanned.map(p => p.num);
+			const {pages: shots} = await parser.getScreenshot({partial: pageNums});
+			if(total <= PDF_OCR_PAGE_THRESHOLD) {
+				return {
+					text: text.trim(),
+					images: shots.map(s => ({mime: 'image/png', data: Buffer.from(s.data).toString('base64')}))
+				};
+			}
+			const ocrText = await Promise.all(shots.map(async (s, i) => {
+				const path = await this.writeTemp(`page-${pageNums[i]}.png`, Buffer.from(s.data));
+				try {
+					return await this.ai.vision.ocr(path) || '';
+				} finally {
+					fs.rm(dirname(path), {recursive: true, force: true}).catch(() => {});
+				}
+			}));
+			return {text: [text.trim(), ...ocrText].filter(Boolean).join('\n\n'), images: []};
+		} finally {
+			await parser.destroy();
+		}
+	}
+
+	private async resolveFile(file: LLMFile): Promise<{text?: string, images?: {mime: string, data: string}[]}> {
+		const name = file.name || (file.path ? basename(file.path) : 'file');
+
+		// Already resolved on a previous turn, reuse cached text
+		if(file.extracted) return {text: `<file name="${name}">\n${file.content}\n</file>`};
+
+		const ext = extname(name).slice(1).toLowerCase();
+		const mime = file.mime || '';
+		const isAudio = mime.startsWith('audio/') || LLM.AUDIO_EXT.includes(ext);
+		const isImage = mime.startsWith('image/') || LLM.IMAGE_EXT.includes(ext);
+		const isPdf = mime === 'application/pdf' || LLM.PDF_EXT.includes(ext);
+		const isText = mime.startsWith('text/') || LLM.TEXT_EXT.includes(ext);
+
+		let tmpDir: string | null = null;
+		try {
+			if(isImage) {
+				const data = (await this.loadBuffer(file, false)).toString('base64');
+				return {images: [{mime: mime || `image/${ext === 'jpg' ? 'jpeg' : ext}`, data}]};
+			}
+
+			if(isPdf) {
+				const {text, images} = await this.resolvePdf(await this.loadBuffer(file, false));
+				// Only cache/skip re-processing when we didn't need to hand off images (OCR'd or fully text-based)
+				if(!images.length) {
+					file.content = text;
+					file.extracted = true;
+					delete file.path;
+				}
+				return {text: `<file name="${name}">\n${text || '[Scanned PDF - see attached page images]'}\n</file>`, images};
+			}
+
+			let text: string;
+			if(isAudio) {
+				let path = file.path;
+				if(!path) {
+					const buffer = await this.loadBuffer(file, false);
+					path = await this.writeTemp(name, buffer);
+					tmpDir = dirname(path);
+				}
+				text = await this.ai.audio.asr(path) || '';
+			} else if(isText) {
+				text = (await this.loadBuffer(file, true)).toString('utf-8');
+			} else {
+				text = `Unsupported file type: ${ext || mime}`;
+			}
+
+			// Cache result, skip re-extraction on future turns of the same conversation
+			file.content = text;
+			file.extracted = true;
+			delete file.path;
+
+			return {text: `<file name="${name}">\n${text}\n</file>`};
+		} catch(err: any) {
+			return {text: `<file name="${name}">Failed to process: ${err.message}</file>`};
+		} finally {
+			if(tmpDir) fs.rm(tmpDir, {recursive: true, force: true}).catch(() => {});
+		}
+	}
+
+	private async resolveFiles(files: LLMFile[]): Promise<{text: string, images: {mime: string, data: string}[]}> {
+		const resolved = await Promise.all(files.map(f => this.resolveFile(f)));
+		return {
+			text: resolved.filter(r => r.text).map(r => r.text).join('\n\n'),
+			images: resolved.flatMap(r => r.images || [])
+		};
+	}
+
 	private setupAgent(agents: Agent[] = [], allAgents: Agent[], history: LLMMessage[], aborts: (() => void)[], depth = 0, delegateState: {resp: string | null}): AiTool[] {
 		return agents.map(a => {
 			const toolName = `${a.delegate ? '' : 'sub'}agent_${snakeCase(a.name)}`;
@@ -207,7 +348,7 @@ ${a.system}`,
 
 		const list = allTools.map(t => `- ${t.name}: ${t.description}`).join('\n');
 		return {
-			prompt: `You have access to the following MCP tools:\n${list}`,
+			prompt: `## MCP\nYou have access to the following MCP tools:\n${list}`,
 			tools: allTools
 		};
 	}
@@ -216,7 +357,7 @@ ${a.system}`,
 		if(!skills?.length) return {prompt: '', tools: []};
 		const list = skills.map(s => `- ${s.name}: ${s.description}`).join('\n');
 		return {
-			prompt: `You have access to the following skill documents, whenever there is overlap between a question and a skill file, use \`skill_read\` to get instructions and background knowledge:\n${list}`,
+			prompt: `## Skills\nYou have access to the following skill documents, whenever there is overlap between a question and a skill file, use \`skill_read\` to get instructions and background knowledge:\n${list}`,
 			tools: [{
 				name: 'skill_read',
 				description: 'Read the full content of a skill/knowledge document',
@@ -316,30 +457,26 @@ ${a.system}`,
 							} else listed.push(r);
 						}
 
-						prompts.unshift(`You have a background memory process which has prefetched relevant information${mem.update ? ' and will create new memories from this conversation' : ''} for you
+						prompts.unshift(`## Memory
+You have a background memory process which has prefetched relevant information${mem.update ? ' and will create new memories from this conversation' : ''} for you
 Assume it is perfect and never mention this process to anyone ever
 Always use your memories to craft a personalized response, they contain links / [[wiki links]] which you use navigate between them
 ${mem.tool ? `You can access memory files via the \`memory_search\` and \`memory_recall\` tools
 When you need information about the user, \`memory_recall\` \`People/User\` before asking (fetch if not included bellow)
-When you need information not provided, attempt 1-3 \`memory_search\` calls with unique queries before asking` : ''}
+When you need information not provided, attempt 1-3 \`memory_search\` calls with distinct queries before asking` : ''}
 
-${preloaded.length ? `
+${preloaded.length ? `### Prefetched Memories (Most relevant first):
-Prefetched Memories (Most relevant first):
 
 ${preloaded.map(r => `Memory: ${r.name}
 Description: ${r.description}
-Linked: ${[r.links, ...r.backlinks].join(', ')}
+Linked: ${makeUnique([...r.links, ...r.backlinks]).join(', ')}
 \`\`\`
-${r.content}
+${stripHeader(r.content)}
 \`\`\``).join('\n\n')}` : ''}
-
+${mem.tool && listed.length ? '\n' + listed.map(r => `Memory: ${r.name}
-${mem.tool && listed.length ? listed.map(r => `Memory: ${r.name}
 Description: ${r.description}
-Linked: ${[r.links, ...r.backlinks].join(', ')}
+Linked: ${makeUnique([...r.links, ...r.backlinks]).join(', ')}
-<!-- Truncated -->`).join('\n\n') : ''}
+<!-- Truncated -->`).join('\n\n') : ''}`.trim())
-
-${mem.tool ? `Full memory list:
-${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}` : ''}`.trim())
 					}
 					if(mem.tool) tools.push(this.memoryManager.tools.read(mem.memory));
 				}
@@ -347,6 +484,19 @@ ${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}` : ''}`.trim())
 
 			if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
 
+			// Files
+			const files = options.files || [];
+			const lastMsg = history[history.length - 1];
+			const originalContent = lastMsg?.content;
+			if(files.length && lastMsg?.role === 'user') {
+				lastMsg.files = files;
+				const {text, images} = await this.resolveFiles(files);
+				const merged = text ? `${originalContent}\n\n${text}` : originalContent;
+				lastMsg.content = images.length
+					? [...images.map(i => ({type: 'image', mime: i.mime, data: i.data})), {type: 'text', text: merged}]
+					: merged;
+			}
+
 			const toolTimings = new Map<string, {duration: number, tps: number}>();
 			tools = this.wrapToolTiming(tools, toolTimings);
 
@@ -356,6 +506,8 @@ ${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}` : ''}`.trim())
 			request = this.models[m].ask('', {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
 			let resp = await request;
 
+			if(files.length && lastMsg?.role === 'user') lastMsg.content = originalContent;
+
 			// Capture meta (duration / tps)
 			for(const h of history) {
 				if(h.role === 'tool' && toolTimings.has(h.id)) Object.assign(h, toolTimings.get(h.id));

src/memory.ts

@@ -61,6 +61,10 @@ function cosineSearch(query: number[], memories: Memory[], limit: number): Memor
 		.map(s => s.ref);
 }
 
+export function stripHeader(content: string): string {
+	return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
+}
+
 export class MemoryCache {
 	private tree!: KDTree<MemoryRef>;
 	public memories: Memory[];
@@ -249,7 +253,7 @@ ${m.content}
 
 	private appendFacts(node: Memory, facts: string[]): void {
 		this.ensureDoc(node);
-		const body = this.stripHeader(node.content);
+		const body = stripHeader(node.content);
 		const bullets = facts.map(f => `- ${f}`).join('\n');
 		const idx = body.indexOf(FACTS_HEADING);
 		const newBody = idx === -1
@@ -355,7 +359,7 @@ ${ghosts.length ? `${ghosts.map(g => `- ${g}: (Ghost)`).join('\n')}` : ''}`,
 	}
 
 	private async docAgent(node: Memory, memories: Memory[], options: LLMRequest, entry: {request: {abort?: () => void} | null}): Promise<void> {
-		const currentBody = this.stripHeader(node.content);
+		const currentBody = stripHeader(node.content);
 		let update;
 		try {
 			for (let i = 0; i < 2 && !update?.content; i++) {
@@ -420,10 +424,6 @@ ${currentBody}
 		return {fm, body: match[2]};
 	}
 
-	private stripHeader(content: string): string {
-		return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
-	}
-
 	private touchHeader(node: Memory, body: string): string {
 		const {fm} = this.parseFrontmatter(node.content);
 		fm.set('name', node.name);

src/open-ai.ts

@@ -25,6 +25,13 @@ export class OpenAi extends LLMProvider {
 		return client;
 	}
 
+	private toWireContent(content: any): any {
+		if(!Array.isArray(content)) return content;
+		return content.map(c => c.type === 'image'
+			? {type: 'image_url', image_url: {url: `data:${c.mime};base64,${c.data}`}}
+			: {type: 'text', text: c.text});
+	}
+
 	/** Convert standard history -> OpenAI wire format */
 	private toWire(history: LLMMessage[], system?: string): any[] {
 		const wire: any[] = [];
@@ -41,7 +48,7 @@ export class OpenAi extends LLMProvider {
 					content: h.error || h.content || '',
 				});
 			} else {
-				wire.push({role: h.role, content: h.content});
+				wire.push({role: h.role, content: this.toWireContent(h.content)});
 			}
 		}
 		return wire;
@@ -58,7 +65,7 @@ export class OpenAi extends LLMProvider {
 			const requestParams: any = {
 				model: options.model || this.model,
 				stream: !!options.stream,
-				max_completion_tokens: options.max_tokens || this.ai.options.llm?.max_tokens || undefined,
+				max_completion_tokens: options.maxTokens || this.ai.options.llm?.maxTokens || undefined,
 				temperature: options.temperature || this.ai.options.llm?.temperature || undefined,
 				tools: tools.map(t => ({
 					type: 'function',