#!/usr/bin/env node
/**
 * Build a vector index over the docs for the in-site Oracle (RAG).
 *
 *   node scripts/build-oracle-index.mjs
 *
 * Defaults to Ollama at https://your-ollama-host.example with model nomic-embed-text.
 * Override via env:
 *   OLLAMA_BASE_URL=...
 *   OLLAMA_EMBED_MODEL=...     (e.g. nomic-embed-text, mxbai-embed-large)
 *   EMBED_PROVIDER=openai      (uses OpenAI embeddings via OPENAI_API_KEY)
 *
 * Output: public/oracle-index.json
 *
 * Soft-fail behaviour: if the embedding provider is unreachable or the model
 * is missing, an empty index is written and the runtime endpoint will operate
 * in chat-only (no-RAG) mode.
 */

import fs from 'node:fs';
import path from 'node:path';
import { fileURLToPath } from 'node:url';
import { chunkFile, walkDocs } from './lib/chunk.mjs';
import { embed, embedConfig } from './lib/providers.mjs';

const __dirname = path.dirname(fileURLToPath(import.meta.url));
const DOCS_DIR = path.resolve(__dirname, '../src/content/docs');
const OUT_FILE = path.resolve(__dirname, '../public/oracle-index.json');
const BATCH = 16;

function embedToBase64(f32) {
	return Buffer.from(new Float32Array(f32).buffer).toString('base64');
}

async function main() {
	const cfg = embedConfig();
	console.log(`Embedding provider: ${cfg.provider}`);
	if (cfg.provider === 'ollama') {
		console.log(`  Ollama: ${cfg.ollamaUrl}`);
		console.log(`  Model:  ${cfg.ollamaEmbedModel}`);
	} else if (cfg.provider === 'openai') {
		console.log(`  OpenAI model: ${cfg.openaiEmbedModel}`);
		if (!cfg.hasOpenAIKey) {
			console.warn('  ⚠ OPENAI_API_KEY missing — writing empty index (chat-only mode).');
			writeEmpty('openai-key-missing');
			return;
		}
	}

	console.log(`Walking ${DOCS_DIR}…`);
	const files = walkDocs(DOCS_DIR);
	console.log(`Found ${files.length} markdown files.`);

	const chunks = files.flatMap((f) => chunkFile(f, DOCS_DIR));
	console.log(`Produced ${chunks.length} chunks.`);

	const embeddings = [];
	try {
		for (let i = 0; i < chunks.length; i += BATCH) {
			const batch = chunks.slice(i, i + BATCH);
			const inputs = batch.map((c) => `${c.pageTitle}\n${c.sectionTitle}\n\n${c.content}`);
			const vecs = await embed(inputs);
			for (const v of vecs) embeddings.push(v);
			process.stdout.write(`\r  embedded ${embeddings.length}/${chunks.length}`);
		}
		process.stdout.write('\n');
	} catch (err) {
		console.error(`\n⚠ Embedding failed: ${err.message}`);
		console.error(`  → writing empty index, Oracle will run in chat-only (no-RAG) mode.`);
		if (cfg.provider === 'ollama') {
			console.error(`  → To fix: pull the embedding model on your Ollama server:`);
			console.error(`        curl ${cfg.ollamaUrl}/api/pull -d '{"name":"${cfg.ollamaEmbedModel}"}'`);
		}
		writeEmpty(err.message);
		return;
	}

	const dim = embeddings[0]?.length ?? 0;
	const out = {
		provider: cfg.provider,
		model: cfg.provider === 'ollama' ? cfg.ollamaEmbedModel : cfg.openaiEmbedModel,
		dim,
		builtAt: new Date().toISOString(),
		chunks: chunks.map((c) => ({
			id: c.id,
			url: c.url,
			pageTitle: c.pageTitle,
			sectionTitle: c.sectionTitle,
			language: c.language,
			content: c.content,
		})),
		embeddings: embeddings.map(embedToBase64),
	};

	fs.mkdirSync(path.dirname(OUT_FILE), { recursive: true });
	fs.writeFileSync(OUT_FILE, JSON.stringify(out));
	const kb = (JSON.stringify(out).length / 1024).toFixed(1);
	console.log(`✔ Wrote ${OUT_FILE} (${kb} KB, dim=${dim})`);
}

function writeEmpty(reason) {
	fs.mkdirSync(path.dirname(OUT_FILE), { recursive: true });
	fs.writeFileSync(
		OUT_FILE,
		JSON.stringify(
			{ provider: null, model: null, dim: 0, builtAt: null, reason, chunks: [], embeddings: [] },
			null,
			2
		)
	);
}

main().catch((e) => {
	console.error(e);
	writeEmpty(e.message);
	process.exit(0); // soft-fail so Docker build doesn't break the site
});