import { connect } from '@/scripts/functions/connect'
import { Article, ArticleHeading, ArticleHeadings } from '@/types/content-types'
import { config } from 'dotenv'
import OpenAI from 'openai'
import path from 'path'
import { LocalIndex } from 'vectra'
import { nicelog } from './nicelog'

config()

const MAX_ARTICLES = Infinity
const INCLUDE_API_CONTENT = true
const INCLUDE_CONTENT = true

const index = new LocalIndex(path.join(process.cwd(), 'utils', 'vector-db'))

const openai = new OpenAI({
	apiKey: process.env.OPENAI_KEY,
})

export class ContentVectorDatabase {
	index: LocalIndex
	api: OpenAI

	constructor(opts = {} as { index: LocalIndex; api: OpenAI }) {
		this.index = opts.index
		this.api = opts.api
	}

	/**
	 * Get a vector from a piece of text from openai.
	 *
	 * @param text The text to get a vector for.
	 *
	 * @returns The vector.
	 */
	async getVectorEmbeddings(inputs: string[]) {
		const response = await this.api.embeddings.create({
			model: 'text-embedding-ada-002',
			input: inputs,
		})
		return response.data.map((d) => d.embedding)
	}

	async addHeadingToIndex(article: Article, heading: ArticleHeading) {
		const id = `${article.id}#${heading.slug}`

		// Skip headings that are already present
		const hash = this.getHashForString(heading.title + heading.slug)
		const existingItem = await this.index.getItem(id)
		if (existingItem) {
			if (existingItem.metadata.hash === hash) {
				nicelog(`Skipping heading ${id} (already present)`)
				return
			}
			await this.index.deleteItem(id)
		}

		nicelog(`Adding headers for ${article.title}#${heading.title}`)
		const vectors = await this.getVectorEmbeddings([article.title + '#' + heading.title])
		this.index.insertItem({
			id,
			vector: vectors[0],
			metadata: { type: 'heading', articleId: article.id, slug: heading.slug, hash },
		})
	}

	/**
	 * Add a text item to the index.
	 *
	 * @param text The text to add to the index.
	 *
	 * @returns The index item.
	 */
	async addArticleToIndex(article: Article, headings: ArticleHeadings) {
		// This is the content that we'll create the embedding for
		let contentToVectorize: string

		if (article.sectionId === 'gen') {
			// For API docs, we'll just use the title, description, and members as the content.
			// We'll also add a note that the content was generated from the API docs, hopefully
			// so that the embedding better reflects searches for api docs.
			contentToVectorize = `Title: ${article.title}\nPackage: @tldraw/${article.categoryId}\nDescription: ${article.description}\nMembers:${article.keywords}\n\n(content generated from API docs)`
		} else {
			// The content is the raw markdown content, which includes all the markdown
			// headings and annotations, though none of the frontmatter. We'll add the
			// frontmatter information again manually. We may need to also add some information
			// about how "important" this article is, relative to related docs or headings.
			contentToVectorize = `Title: ${article.title}\nDescription: ${article.description}\nKeywords:${article.keywords}\nMarkdown:\n${article.content}`
		}

		if (headings.length) {
			for (const heading of headings) {
				await this.addHeadingToIndex(article, heading)
			}
		}

		// Generate a hash based on the content that we'd be vectorizing
		const hash = this.getHashForString(contentToVectorize)

		// Create chunks from the content; openAI has a limit of 500 tokens per request
		const chunksToAdd: string[] = []
		const chunkSize = 500
		for (let i = 0; i < contentToVectorize.length; i += chunkSize) {
			const chunk = contentToVectorize.slice(i, i + chunkSize)
			chunksToAdd.push(chunk)
		}

		// Is there already an item with this id?
		const existingItem = await this.index.getItem(article.id + '_0')

		if (existingItem) {
			// ...and if the existing item matches our hash, we can skip it
			if (existingItem.metadata.hash === hash) {
				nicelog(`Skipping ${article.id} (already present)`)
				return
			}

			// ...otherwise, delete all the chunks so that we can add a new one.
			for (let i = 0; i < chunksToAdd.length; i++) {
				await this.index.deleteItem(article.id + '_' + i)
			}
		}

		// Add chunks to index
		nicelog(`Adding article ${article.title} (${chunksToAdd.length} chunks)`)

		// Get an embedding / vector for all of the chunks
		const vectors = await this.getVectorEmbeddings(chunksToAdd)

		for (let i = 0; i < vectors.length; i++) {
			const vector = vectors[i]
			// Add the article item to the index (include the hash as metadata)
			await this.index.insertItem({
				id: article.id + '_' + i,
				vector: vector,
				metadata: { type: 'article', articleId: article.id, hash },
			})
		}

		// Sleep for 50ms or so to avoid rate limiting
		await new Promise((r) => setTimeout(r, 35))

		return
	}

	/**
	 * Query an item using our index.
	 *
	 * @param text The text to query.
	 *
	 * @returns The query results.
	 */
	async query(text: string, limit = 5) {
		const vector = await this.getVectorEmbeddings([text])
		const results = await this.index.queryItems(vector[0], limit)
		const output: (
			| { id: string; type: 'article'; score: number }
			| { id: string; type: 'heading'; slug: string; score: number }
		)[] = []
		const visited = new Set<string>()
		for (const result of results) {
			const id = result.item.metadata.articleId as string
			const type = result.item.metadata.type as 'article' | 'heading'
			if (type === 'heading') {
				const slug = result.item.metadata.slug as string
				output.push({ id, type, slug, score: result.score })
			} else {
				// multiple chunks may have been returned
				if (visited.has(id)) continue
				output.push({ id, type, score: result.score })
				visited.add(id)
			}
		}
		return output
	}

	/**
	 * Hash a string using the FNV-1a algorithm.
	 *
	 * @public
	 */
	getHashForString(string: string) {
		let hash = 0
		for (let i = 0; i < string.length; i++) {
			hash = (hash << 5) - hash + string.charCodeAt(i)
			hash |= 0 // Convert to 32bit integer
		}
		return hash + ''
	}
}

let _cvdb: ContentVectorDatabase

export async function getVectorDb(
	opts = {} as {
		updateContent?: boolean
		rebuildIndex?: boolean
	}
) {
	if (_cvdb) {
		return _cvdb
	}

	if (opts.rebuildIndex || !(await index.isIndexCreated())) {
		await index.createIndex({ deleteIfExists: opts.rebuildIndex, version: 1 })
	}

	_cvdb = new ContentVectorDatabase({ api: openai, index })

	if (opts.updateContent || opts.rebuildIndex) {
		nicelog(`Rebuilding index`)
		const db = await connect({ reset: false })

		nicelog(`Getting articles`)
		const articles =
			INCLUDE_API_CONTENT && INCLUDE_CONTENT
				? await db.all('SELECT * FROM articles')
				: INCLUDE_API_CONTENT
					? await db.all('SELECT * FROM articles WHERE articles.sectionId = ?', 'gen')
					: await db.all('SELECT * FROM articles WHERE articles.sectionId != ?', 'gen')

		nicelog(`Adding articles to index`)
		const max = Math.min(articles.length, MAX_ARTICLES)
		for (let i = 0; i < max; i++) {
			const article = articles[i]
			const headings = await db.all(
				'SELECT * FROM headings WHERE articleId = ? AND slug NOT IN (?, ?, ?, ?)',
				article.id,
				'constructor',
				'properties',
				'example',
				'methods'
			)
			nicelog(`Adding article ${article.id} to index (${i} of ${max})`)
			await _cvdb.addArticleToIndex(article, headings)
		}
	}

	return _cvdb
}