import { GoogleGenerativeAI } from '@google/generative-ai'
import { OpenAI } from 'openai'

import { EmbeddingModel } from '../../types/embedding'
import {
	LLMAPIKeyNotSetException,
	LLMBaseUrlNotSetException,
	LLMRateLimitExceededException,
} from '../llm/exception'
import { NoStainlessOpenAI } from '../llm/ollama'

export const getEmbeddingModel = (
	embeddingModelId: string,
	apiKeys: {
		openAIApiKey: string
		geminiApiKey: string
	},
	ollamaBaseUrl: string,
): EmbeddingModel => {
	switch (embeddingModelId) {
		case 'text-embedding-3-small': {
			const openai = new OpenAI({
				apiKey: apiKeys.openAIApiKey,
				dangerouslyAllowBrowser: true,
			})
			return {
				id: 'text-embedding-3-small',
				dimension: 1536,
				getEmbedding: async (text: string) => {
					try {
						if (!openai.apiKey) {
							throw new LLMAPIKeyNotSetException(
								'OpenAI API key is missing. Please set it in settings menu.',
							)
						}
						const embedding = await openai.embeddings.create({
							model: 'text-embedding-3-small',
							input: text,
						})
						return embedding.data[0].embedding
					} catch (error) {
						if (
							error.status === 429 &&
							error.message.toLowerCase().includes('rate limit')
						) {
							throw new LLMRateLimitExceededException(
								'OpenAI API rate limit exceeded. Please try again later.',
							)
						}
						throw error
					}
				},
			}
		}
		case 'text-embedding-004': {
			const client = new GoogleGenerativeAI(apiKeys.geminiApiKey)
			const model = client.getGenerativeModel({ model: 'text-embedding-004' })
			return {
				id: 'text-embedding-004',
				dimension: 768,
				getEmbedding: async (text: string) => {
					try {
						const response = await model.embedContent(text)
						return response.embedding.values
					} catch (error) {
						if (
							error.status === 429 &&
							error.message.includes('RATE_LIMIT_EXCEEDED')
						) {
							throw new LLMRateLimitExceededException(
								'Gemini API rate limit exceeded. Please try again later.',
							)
						}
						throw error
					}
				},
			}
		}
		case 'nomic-embed-text': {
			const openai = new NoStainlessOpenAI({
				apiKey: '',
				dangerouslyAllowBrowser: true,
				baseURL: `${ollamaBaseUrl}/v1`,
			})
			return {
				id: 'nomic-embed-text',
				dimension: 768,
				getEmbedding: async (text: string) => {
					if (!ollamaBaseUrl) {
						throw new LLMBaseUrlNotSetException(
							'Ollama Address is missing. Please set it in settings menu.',
						)
					}
					const embedding = await openai.embeddings.create({
						model: 'nomic-embed-text',
						input: text,
					})
					return embedding.data[0].embedding
				},
			}
		}
		case 'mxbai-embed-large': {
			const openai = new NoStainlessOpenAI({
				apiKey: '',
				dangerouslyAllowBrowser: true,
				baseURL: `${ollamaBaseUrl}/v1`,
			})
			return {
				id: 'mxbai-embed-large',
				dimension: 1024,
				getEmbedding: async (text: string) => {
					if (!ollamaBaseUrl) {
						throw new LLMBaseUrlNotSetException(
							'Ollama Address is missing. Please set it in settings menu.',
						)
					}
					const embedding = await openai.embeddings.create({
						model: 'mxbai-embed-large',
						input: text,
					})
					return embedding.data[0].embedding
				},
			}
		}
		case 'bge-m3': {
			const openai = new NoStainlessOpenAI({
				apiKey: '',
				dangerouslyAllowBrowser: true,
				baseURL: `${ollamaBaseUrl}/v1`,
			})
			return {
				id: 'bge-m3',
				dimension: 1024,
				getEmbedding: async (text: string) => {
					if (!ollamaBaseUrl) {
						throw new LLMBaseUrlNotSetException(
							'Ollama Address is missing. Please set it in settings menu.',
						)
					}
					const embedding = await openai.embeddings.create({
						model: 'bge-m3',
						input: text,
					})
					return embedding.data[0].embedding
				},
			}
		}
		default:
			throw new Error('Invalid embedding model')
	}
}