// Unified provider abstraction for chat and embeddings.
// Used by build-oracle-index.mjs (build time) and src/pages/api/oracle.ts (runtime).

const DEFAULT_OLLAMA_URL    = 'https://api.neuronetz.ai';
const DEFAULT_OLLAMA_CHAT   = 'qwen2.5-coder:14b';
const DEFAULT_OLLAMA_EMBED  = 'nomic-embed-text';
const DEFAULT_ANTHROPIC     = 'claude-haiku-4-5-20251001';
const DEFAULT_OPENAI_EMBED  = 'text-embedding-3-small';

export function llmConfig() {
	return {
		provider: process.env.LLM_PROVIDER ?? 'ollama',
		ollamaUrl: process.env.OLLAMA_BASE_URL ?? DEFAULT_OLLAMA_URL,
		ollamaChatModel: process.env.OLLAMA_CHAT_MODEL ?? DEFAULT_OLLAMA_CHAT,
		anthropicModel: process.env.ANTHROPIC_MODEL ?? DEFAULT_ANTHROPIC,
		hasAnthropicKey: !!process.env.ANTHROPIC_API_KEY,
	};
}

export function embedConfig() {
	const provider = process.env.EMBED_PROVIDER ?? 'ollama';
	return {
		provider,
		ollamaUrl: process.env.OLLAMA_BASE_URL ?? DEFAULT_OLLAMA_URL,
		ollamaEmbedModel: process.env.OLLAMA_EMBED_MODEL ?? DEFAULT_OLLAMA_EMBED,
		openaiEmbedModel: process.env.OPENAI_EMBED_MODEL ?? DEFAULT_OPENAI_EMBED,
		hasOpenAIKey: !!process.env.OPENAI_API_KEY,
	};
}

// ---------------------------------------------------------------------------
// Embeddings
// ---------------------------------------------------------------------------

async function ollamaEmbedBatch(baseUrl, model, inputs) {
	const out = [];
	// Ollama /api/embeddings is single-input. Batch by looping.
	for (const text of inputs) {
		const res = await fetch(`${baseUrl.replace(/\/$/, '')}/api/embeddings`, {
			method: 'POST',
			headers: { 'Content-Type': 'application/json' },
			body: JSON.stringify({ model, prompt: text }),
		});
		if (!res.ok) {
			const body = await res.text();
			throw new Error(`Ollama embeddings ${res.status}: ${body}`);
		}
		const data = await res.json();
		if (!Array.isArray(data.embedding)) {
			throw new Error(`Ollama embeddings: unexpected response: ${JSON.stringify(data).slice(0, 200)}`);
		}
		out.push(data.embedding);
	}
	return out;
}

async function openaiEmbedBatch(model, inputs) {
	const { default: OpenAI } = await import('openai');
	const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
	const res = await client.embeddings.create({ model, input: inputs });
	return res.data.map((d) => d.embedding);
}

export async function embed(inputs, opts = {}) {
	const cfg = embedConfig();
	const provider = opts.provider ?? cfg.provider;
	const list = Array.isArray(inputs) ? inputs : [inputs];

	if (provider === 'ollama') {
		return ollamaEmbedBatch(cfg.ollamaUrl, cfg.ollamaEmbedModel, list);
	}
	if (provider === 'openai') {
		if (!cfg.hasOpenAIKey) throw new Error('OPENAI_API_KEY not set.');
		return openaiEmbedBatch(cfg.openaiEmbedModel, list);
	}
	throw new Error(`Unknown EMBED_PROVIDER: ${provider}`);
}

// ---------------------------------------------------------------------------
// Chat
// ---------------------------------------------------------------------------

export async function chat({ system, messages, maxTokens = 800 }) {
	const cfg = llmConfig();

	if (cfg.provider === 'ollama') {
		const url = `${cfg.ollamaUrl.replace(/\/$/, '')}/api/chat`;
		const ollamaMessages = [];
		if (system) ollamaMessages.push({ role: 'system', content: system });
		for (const m of messages) {
			if (m.role === 'user' || m.role === 'assistant') {
				ollamaMessages.push({ role: m.role, content: m.content });
			}
		}
		const res = await fetch(url, {
			method: 'POST',
			headers: { 'Content-Type': 'application/json' },
			body: JSON.stringify({
				model: cfg.ollamaChatModel,
				messages: ollamaMessages,
				stream: false,
				options: { num_predict: maxTokens, temperature: 0.4 },
			}),
		});
		if (!res.ok) {
			const body = await res.text();
			throw new Error(`Ollama chat ${res.status}: ${body}`);
		}
		const data = await res.json();
		return {
			text: data.message?.content ?? '',
			model: cfg.ollamaChatModel,
			provider: 'ollama',
		};
	}

	if (cfg.provider === 'anthropic') {
		if (!cfg.hasAnthropicKey) throw new Error('ANTHROPIC_API_KEY not set.');
		const { default: Anthropic } = await import('@anthropic-ai/sdk');
		const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
		const apiMessages = messages
			.filter((m) => m.role === 'user' || m.role === 'assistant')
			.map((m) => ({ role: m.role, content: m.content }));
		const completion = await client.messages.create({
			model: cfg.anthropicModel,
			max_tokens: maxTokens,
			system,
			messages: apiMessages.length ? apiMessages : [{ role: 'user', content: '' }],
		});
		const text = completion.content
			.filter((p) => p.type === 'text')
			.map((p) => p.text)
			.join('\n');
		return { text, model: cfg.anthropicModel, provider: 'anthropic' };
	}

	throw new Error(`Unknown LLM_PROVIDER: ${cfg.provider}`);
}
