import Logger from '@joplin/utils/Logger';
import NoteEmbedding from '../../models/NoteEmbedding';
import Note from '../../models/Note';
import Tag from '../../models/Tag';
import AiService from './AiService';
import { EmbeddingProvider } from './types';
import { EmbeddingsPage, GetEmbeddingsOptions, SearchOptions, SearchQuery, SearchRelevance, SearchResult, SearchScope } from '../plugins/api/types';
import { splitByMarkdownFormattingApproximate } from '../../string-utils';

export type { EmbeddingsPage, GetEmbeddingsOptions, SearchOptions, SearchQuery, SearchRelevance, SearchResult, SearchScope };

const logger = Logger.create('SearchService');

// Semantic search over the local embedding index.
// The "relevance" preset is the plugin-facing contract; we own the mapping
// to model-specific (k, minScore) so plugins survive model changes.

interface RelevanceTuning {
	k: number;
	minScore: number;
}

// Tuned for multilingual-e5-small. Becomes a per-model map when we add more.
// multilingual-e5-small usually returns results in the range [0.7,1]
const RELEVANCE_DEFAULTS: Record<SearchRelevance, RelevanceTuning> = {
	strict: { k: 5, minScore: 0.86 },
	normal: { k: 10, minScore: 0.83 },
	loose: { k: 20, minScore: 0.74 },
};

const DEFAULT_EMBEDDINGS_PAGE_SIZE = 500;
const MAX_EMBEDDINGS_PAGE_SIZE = 5000;

// Opaque to plugins: the cursor format is an implementation detail and may
// change. The `v1:` prefix lets a future format coexist via a version bump.
const encodeEmbeddingsCursor = (rowid: number) => `v1:${rowid}`;

const decodeEmbeddingsCursor = (cursor: string | undefined) => {
	if (!cursor) return undefined;
	const match = /^v1:(\d+)$/.exec(cursor);
	if (!match) throw new Error(`Invalid embeddings cursor: ${JSON.stringify(cursor)}`);
	return Number(match[1]);
};

const resolveEmbeddingsLimit = (raw: number | undefined) => {
	if (raw === undefined) return DEFAULT_EMBEDDINGS_PAGE_SIZE;
	if (!Number.isInteger(raw) || raw < 1) {
		throw new Error(`Invalid embeddings limit: ${raw}`);
	}
	return Math.min(raw, MAX_EMBEDDINGS_PAGE_SIZE);
};

// vec0 returns L2 distance. Our vectors are L2-normalised, so cosine
// similarity = 1 − d²/2 exactly. Clamp to handle float drift on self-matches
// and opposite-vector edges.
const cosineFromDistance = (distance: number) => {
	const score = 1 - (distance * distance) / 2;
	if (score < 0) return 0;
	if (score > 1) return 1;
	return score;
};

const cosineSimilarity = (a: number[], b: number[]) => {
	const dot = (a: number[], b: number[]) => {
		if (a.length !== b.length) throw new Error(`Length mismatch: ${a.length} != ${b.length}`);

		let sum = 0;
		for (let i = 0; i < a.length; i++) {
			sum += a[i] * b[i];
		}

		return sum;
	};
	const norm = (v: number[]) => Math.sqrt(dot(v, v));

	return dot(a, b) / norm(a) / norm(b);
};

class MissingProviderError extends Error {
	public constructor() {
		super('No embedding provider is active. Enable AI features in Settings → AI.');
	}
}

export default class SearchService {

	private static instance_: SearchService;

	public static instance(): SearchService {
		if (!this.instance_) this.instance_ = new SearchService();
		return this.instance_;
	}

	public async getEmbeddings(options: GetEmbeddingsOptions = {}): Promise<EmbeddingsPage> {
		const provider = AiService.instance().getActiveEmbeddingProvider();
		if (!provider) {
			throw new MissingProviderError();
		}

		const limit = resolveEmbeddingsLimit(options.limit);
		const afterRowid = decodeEmbeddingsCursor(options.cursor);

		// Fetch one extra row to detect end-of-stream without an extra round-
		// trip: if the DB returns limit+1, there's at least one more page; if
		// it returns ≤limit, we know this is the last page.
		const fetched = await NoteEmbedding.chunksPage({
			noteIds: options.noteIds,
			afterRowid,
			limit: limit + 1,
		});
		const hasMore = fetched.length > limit;
		const rows = hasMore ? fetched.slice(0, limit) : fetched;

		// Take the page's modelId from the rows themselves rather than the
		// live provider: a model swap during the await could leave rows from
		// the old model in flight while provider.modelId already reports the
		// new one. Empty pages have no rows to read from, so fall back to the
		// provider (no rows = no mismatch to expose).
		let modelId: string;
		let dimension: number;
		if (rows.length === 0) {
			modelId = provider.modelId;
			dimension = provider.dimension;
		} else {
			modelId = rows[0].modelId;
			dimension = rows[0].vector.length;
			for (const r of rows) {
				if (r.modelId !== modelId) {
					throw new Error(`Embeddings page spans multiple models (${modelId} and ${r.modelId}). The index is being rebuilt — retry shortly.`);
				}
			}
		}

		const chunks = rows.map(r => ({
			noteId: r.noteId,
			chunkIndex: r.chunkIndex,
			chunkText: r.chunkText,
			vector: r.vector,
		}));

		const nextCursor = hasMore
			? encodeEmbeddingsCursor(rows[rows.length - 1].rowid)
			: undefined;

		return {
			modelId,
			dimension,
			chunks,
			nextCursor,
		};
	}

	public async search(options: SearchOptions): Promise<SearchResult[]> {
		const provider = AiService.instance().getActiveEmbeddingProvider();
		if (!provider) {
			throw new Error('No embedding provider is active. Enable AI features in Settings → AI.');
		}

		const relevance = options.relevance ?? 'normal';
		const tuning = RELEVANCE_DEFAULTS[relevance];

		const queryVectors = await this.resolveQueryVectors(options.query, provider);
		if (!queryVectors.length) return [];

		const noteIds = await this.resolveScope(options.scope);
		// Empty scope = search nothing (e.g. tag with no notes).
		if (noteIds && noteIds.length === 0) return [];

		// noteId queries produce one vector per chunk; merge by (note, chunk),
		// keeping the highest score seen.
		const best = new Map<string, SearchResult>();
		for (const queryVector of queryVectors) {
			const hits = await NoteEmbedding.similaritySearch(queryVector, {
				k: tuning.k,
				noteIds: noteIds ?? undefined,
			});
			for (const hit of hits) {
				const score = cosineFromDistance(hit.distance);
				if (score < tuning.minScore) continue;
				const key = `${hit.noteId}:${hit.chunkIndex}`;
				const existing = best.get(key);
				if (!existing || score > existing.score) {
					best.set(key, {
						noteId: hit.noteId,
						chunkIndex: hit.chunkIndex,
						chunkText: hit.chunkText,
						score,
					});
				}
			}
		}

		return Array.from(best.values())
			.sort((a, b) => b.score - a.score)
			.slice(0, tuning.k);
	}

	public async bestMatchInResult(query: string, result: SearchResult) {
		return this.bestSubMatchInText_(query, result.chunkText);
	}

	private async bestSubMatchInText_(query: string, text: string) {
		const provider = AiService.instance().getActiveEmbeddingProvider();
		if (!provider) {
			throw new MissingProviderError();
		}

		const queryVectors = await this.resolveQueryVectors({ text: query }, provider);

		const scoreChunk = (embedding: number[]) => {
			let bestScore = 0;
			for (const query of queryVectors) {
				const score = cosineSimilarity(query, embedding);
				if (score >= bestScore) {
					bestScore = score;
				}
			}
			return bestScore;
		};

		const findBestIndex = (scores: number[]) => {
			let bestIndex = 0;
			let bestScore = -Infinity;
			for (let i = 0; i < scores.length; i++) {
				if (scores[i] > bestScore) {
					bestScore = scores[i];
					bestIndex = i;
				}
			}
			return bestIndex;
		};

		const getBestSubtext = async (chunks: string[]) => {
			const embeddings = await provider.embed(chunks);
			const scores = embeddings.map(scoreChunk);

			return chunks[findBestIndex(scores)];
		};

		const lines = text.split('\n')
			.map(line => line.trim())
			.filter(line => line.length > 0);
		if (lines.length) {
			text = await getBestSubtext(lines);
		}

		// Try to get a sub-section that doesn't include Markdown formatting. Results including
		// Markdown are more difficult to highlight after rendering:
		const segments = splitByMarkdownFormattingApproximate(text)
			.map(segment => segment.trim())
			// Marking short or single-character matches usually isn't helpful
			.filter(segment => segment.length > 1);
		if (segments.length > 1) {
			text = await getBestSubtext(segments);
		} else if (segments.length > 0) {
			text = segments[0];
		}

		return text;
	}

	private async resolveQueryVectors(
		query: SearchQuery,
		provider: EmbeddingProvider,
	): Promise<number[][]> {
		if ('text' in query) {
			if (!query.text.trim()) return [];
			// Asymmetric providers (e5) get better retrieval with embedQuery;
			// symmetric ones fall back to embed.
			const embedQuery = provider.embedQuery?.bind(provider) ?? provider.embed.bind(provider);
			return embedQuery([query.text]);
		}

		// Reuse stored vectors so the math stays symmetric and we avoid a
		// re-embed pass.
		const vectors = await NoteEmbedding.vectorsByNoteId(query.noteId);
		if (!vectors.length) {
			logger.info(`No embeddings indexed for note ${query.noteId} — returning empty result`);
		}
		return vectors;
	}

	private async resolveScope(scope: SearchScope | undefined): Promise<string[] | null> {
		if (!scope || scope.type === 'all') return null;
		switch (scope.type) {
		case 'note':
			return [scope.noteId];
		case 'folder': {
			const notes = await Note.previews(scope.folderId, { fields: ['id'] });
			return notes.map(n => n.id);
		}
		case 'tag':
			return Tag.noteIds(scope.tagId);
		}
	}
}