import fs from 'node:fs';
import os from 'node:os';
import path from 'node:path';
import { getToken } from './token';

interface CopilotConfig {
  copilot_tokens?: Record<string, string>;
}

/**
 * Read the GitHub Copilot OAuth token from the CLI config file.
 * Stored at ~/.copilot/config.json under copilot_tokens["{host}:{login}"].
 * Returns the first available token, or null if unavailable.
 */
function readCopilotToken(): string | null {
  try {
    const raw = fs.readFileSync(
      path.join(os.homedir(), '.copilot', 'config.json'),
      'utf-8',
    );
    const cfg = JSON.parse(raw) as CopilotConfig;
    const vals = Object.values(cfg.copilot_tokens ?? {});
    return vals[0] ?? null;
  } catch {
    return null;
  }
}

/**
 * Embed a single text string using the best available credentials.
 *
 * Priority:
 *  1. GitHub Copilot CLI token → OpenAI-compatible embeddings endpoint at
 *     https://api.githubcopilot.com
 *  2. Stored OpenAI token → standard OpenAI embeddings API
 *
 * Throws if no credentials are available or the API call fails.
 * Callers must .catch() this and handle the error without rejecting
 * the surrounding tRPC mutation.
 */
export async function embedText(text: string): Promise<number[]> {
  const { OpenAIEmbeddings } = await import('@langchain/openai');

  const copilotToken = readCopilotToken();

  let embeddingsInstance;
  if (copilotToken) {
    embeddingsInstance = new OpenAIEmbeddings({
      apiKey: copilotToken,
      model: 'text-embedding-3-small',
      configuration: { baseURL: 'https://api.githubcopilot.com' },
    });
  } else {
    const openaiToken = await getToken('openai');
    if (!openaiToken) {
      throw new Error(
        '[Embeddings] No credentials available. Authenticate with Copilot CLI or add an OpenAI token in Settings.',
      );
    }
    embeddingsInstance = new OpenAIEmbeddings({
      apiKey: openaiToken,
      model: 'text-embedding-3-small',
    });
  }

  // embedDocuments returns number[][] — cast explicitly to satisfy strict TS
  const results = (await embeddingsInstance.embedDocuments([text])) as number[][];
  const vector = results[0] as number[] | undefined;
  if (!vector || vector.length === 0) {
    throw new Error('[Embeddings] Empty vector returned from embedding API');
  }
  return vector;
}