/**
 * Embedding provider interface and implementations for multiple backends.
 */

import type { EmbeddingConfig } from '../config/schema.js';

/**
 * Interface for embedding providers that convert text to vectors.
 */
export interface EmbeddingProvider {
  /** Generate embeddings for one or more texts. Returns one vector per text. */
  embed(texts: string[]): Promise<number[][]>;
  /** The dimensionality of the embedding vectors. */
  dimensions: number;
}

// ---------------------------------------------------------------------------
// OpenAI
// ---------------------------------------------------------------------------

export class OpenAIEmbeddingProvider implements EmbeddingProvider {
  private _model: string;
  private _dimensions: number;
  private _apiKey: string;
  private _endpoint?: string;

  constructor(config: EmbeddingConfig) {
    this._model = config.model;
    this._dimensions = config.dimensions ?? 1536;
    this._apiKey = config.api_key ?? process.env.OPENAI_API_KEY ?? '';
    this._endpoint = config.endpoint;
  }

  get dimensions(): number {
    return this._dimensions;
  }

  async embed(texts: string[]): Promise<number[][]> {
    const { default: OpenAI } = await import('openai');
    const client = new OpenAI({
      apiKey: this._apiKey,
      ...(this._endpoint ? { baseURL: this._endpoint } : {}),
    });

    const response = await client.embeddings.create({
      model: this._model,
      input: texts,
      ...(this._dimensions ? { dimensions: this._dimensions } : {}),
    });

    // Sort by index to ensure order matches input
    const sorted = response.data.sort((a, b) => a.index - b.index);
    return sorted.map((item) => item.embedding);
  }
}

// ---------------------------------------------------------------------------
// Gemini
// ---------------------------------------------------------------------------

export class GeminiEmbeddingProvider implements EmbeddingProvider {
  private _model: string;
  private _dimensions: number;
  private _apiKey: string;

  constructor(config: EmbeddingConfig) {
    this._model = config.model;
    this._dimensions = config.dimensions ?? 768;
    this._apiKey = config.api_key ?? process.env.GOOGLE_API_KEY ?? '';
  }

  get dimensions(): number {
    return this._dimensions;
  }

  async embed(texts: string[]): Promise<number[][]> {
    const { GoogleGenerativeAI } = await import('@google/generative-ai');
    const genAI = new GoogleGenerativeAI(this._apiKey);
    const model = genAI.getGenerativeModel({ model: this._model });

    // Use batchEmbedContents for efficiency
    const requests = texts.map((text) => ({
      content: { role: 'user' as const, parts: [{ text }] },
    }));

    const response = await model.batchEmbedContents({ requests });
    return response.embeddings.map((e) => e.values);
  }
}

// ---------------------------------------------------------------------------
// Ollama
// ---------------------------------------------------------------------------

export class OllamaEmbeddingProvider implements EmbeddingProvider {
  private _model: string;
  private _dimensions: number;
  private _host?: string;

  constructor(config: EmbeddingConfig) {
    this._model = config.model;
    this._dimensions = config.dimensions ?? 768;
    this._host = config.endpoint;
  }

  get dimensions(): number {
    return this._dimensions;
  }

  async embed(texts: string[]): Promise<number[][]> {
    const { Ollama } = await import('ollama');
    const client = new Ollama({ host: this._host });

    const response = await client.embed({
      model: this._model,
      input: texts,
    });

    return response.embeddings;
  }
}

// ---------------------------------------------------------------------------
// LlamaCpp
// ---------------------------------------------------------------------------

export class LlamaCppEmbeddingProvider implements EmbeddingProvider {
  private _dimensions: number;
  private _endpoint: string;

  constructor(config: EmbeddingConfig) {
    this._dimensions = config.dimensions ?? 768;
    this._endpoint = config.endpoint ?? 'http://localhost:8080';
  }

  get dimensions(): number {
    return this._dimensions;
  }

  async embed(texts: string[]): Promise<number[][]> {
    const results: number[][] = [];

    for (const text of texts) {
      const response = await fetch(`${this._endpoint}/embedding`, {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ content: text }),
      });

      if (!response.ok) {
        throw new Error(`LlamaCpp embedding request failed: ${response.status} ${response.statusText}`);
      }

      const data = (await response.json()) as { embedding: number[] };
      results.push(data.embedding);
    }

    return results;
  }
}

// ---------------------------------------------------------------------------
// Factory
// ---------------------------------------------------------------------------

/**
 * Create an embedding provider from config.
 */
export function createEmbeddingProvider(config: EmbeddingConfig): EmbeddingProvider {
  switch (config.provider) {
    case 'openai':
      return new OpenAIEmbeddingProvider(config);
    case 'gemini':
      return new GeminiEmbeddingProvider(config);
    case 'ollama':
      return new OllamaEmbeddingProvider(config);
    case 'llamacpp':
      return new LlamaCppEmbeddingProvider(config);
    default:
      throw new Error(`Unknown embedding provider: ${(config as Record<string, unknown>).provider}`);
  }
}