using ChatRAG.Contracts.VectorSearch;
using ChatRAG.Data;
using ChatRAG.Models;
using Microsoft.Extensions.VectorData;
using Microsoft.SemanticKernel.Embeddings;

#pragma warning disable SKEXP0001 // Type is for evaluation purposes only and is subject to change or removal in future updates. Suppress this diagnostic to proceed.
namespace ChatRAG.Services.SearchVectors
{
    public class MongoVectorSearchService : IVectorSearchService
    {
        private readonly TextDataRepository _textDataRepository;
        private readonly ITextEmbeddingGenerationService _embeddingService;

        public MongoVectorSearchService(
            TextDataRepository textDataRepository,
            ITextEmbeddingGenerationService embeddingService)
        {
            _textDataRepository = textDataRepository;
            _embeddingService = embeddingService;
        }

        // ... resto da implementação permanece igual ...
        // (copiar do código anterior)

        public async Task<List<VectorSearchResult>> SearchSimilarAsync(
            double[] queryEmbedding,
            string? projectId = null,
            double threshold = 0.3,
            int limit = 5,
            Dictionary<string, object>? filters = null)
        {
            List<TextoComEmbedding> textos = null;
            try
            {
                textos = string.IsNullOrEmpty(projectId)
                    ? await _textDataRepository.GetAsync()
                    : await _textDataRepository.GetByProjectIdAsync(projectId);
            }
            catch (Exception ex)
            {
                throw new Exception($"Erro ao buscar documentos: {ex.Message}");
            }

            var resultados = textos
                .Select(texto => new VectorSearchResult
                {
                    Id = texto.Id,
                    Title = texto.Titulo,
                    Content = texto.Conteudo,
                    ProjectId = texto.ProjetoId,
                    Score = CalcularSimilaridadeCoseno(queryEmbedding, texto.Embedding),
                    Embedding = texto.Embedding,
                    Provider = "MongoDB",
                    CreatedAt = DateTime.UtcNow,
                    UpdatedAt = DateTime.UtcNow
                })
                .Where(r => r.Score >= threshold)
                .OrderByDescending(r => r.Score)
                .Take(limit)
                .ToList();

            return resultados;
        }

        public async Task<List<VectorSearchResult>> SearchSimilarDynamicAsync(
            double[] queryEmbedding,
            string projectId,
            double minThreshold = 0.5,
            int limit = 5)
        {
            var resultados = await SearchSimilarAsync(queryEmbedding, projectId, minThreshold, limit);

            if (resultados.Count < 3)
            {
                resultados = await SearchSimilarAsync(queryEmbedding, projectId, minThreshold * 0.7, limit);
            }

            if (resultados.Count < 3)
            {
                resultados = await SearchSimilarAsync(queryEmbedding, projectId, 0.2, limit);
            }

            return resultados.Take(limit).ToList();
        }

        public async Task<string> AddDocumentAsync(
            string title,
            string content,
            string projectId,
            double[] embedding,
            Dictionary<string, object>? metadata = null)
        {
            var documento = new TextoComEmbedding
            {
                Id = Guid.NewGuid().ToString(),
                Titulo = title,
                Conteudo = content,
                ProjetoId = projectId,
                Embedding = embedding
            };

            await _textDataRepository.CreateAsync(documento);
            return documento.Id;
        }

        public async Task UpdateDocumentAsync(
            string id,
            string title,
            string content,
            string projectId,
            double[] embedding,
            Dictionary<string, object>? metadata = null)
        {
            var documento = new TextoComEmbedding
            {
                Id = id,
                Titulo = title,
                Conteudo = content,
                ProjetoId = projectId,
                Embedding = embedding
            };

            await _textDataRepository.UpdateAsync(id, documento);
        }

        public async Task DeleteDocumentAsync(string id)
        {
            await _textDataRepository.RemoveAsync(id);
        }

        public async Task<bool> DocumentExistsAsync(string id)
        {
            var doc = await _textDataRepository.GetAsync(id);
            return doc != null;
        }

        public async Task<VectorSearchResult?> GetDocumentAsync(string id)
        {
            var doc = await _textDataRepository.GetAsync(id);
            if (doc == null) return null;

            return new VectorSearchResult
            {
                Id = doc.Id,
                Title = doc.Titulo,
                Content = doc.Conteudo,
                ProjectId = doc.ProjetoId,
                Score = 1.0,
                Embedding = doc.Embedding,
                Provider = "MongoDB",
                CreatedAt = DateTime.UtcNow,
                UpdatedAt = DateTime.UtcNow
            };
        }

        public async Task<List<VectorSearchResult>> GetDocumentsByProjectAsync(string projectId)
        {
            var docs = await _textDataRepository.GetByProjectIdAsync(projectId);
            return docs.Select(doc => new VectorSearchResult
            {
                Id = doc.Id,
                Title = doc.Titulo,
                Content = doc.Conteudo,
                ProjectId = doc.ProjetoId,
                Score = 1.0,
                Embedding = doc.Embedding,
                Provider = "MongoDB",
                CreatedAt = DateTime.UtcNow,
                UpdatedAt = DateTime.UtcNow
            }).ToList();
        }

        public async Task<int> GetDocumentCountAsync(string? projectId = null)
        {
            if (string.IsNullOrEmpty(projectId))
            {
                var all = await _textDataRepository.GetAsync();
                return all.Count;
            }
            else
            {
                var byProject = await _textDataRepository.GetByProjectIdAsync(projectId);
                return byProject.Count;
            }
        }

        public async Task<bool> IsHealthyAsync()
        {
            try
            {
                var count = await GetDocumentCountAsync();
                return true;
            }
            catch
            {
                return false;
            }
        }

        public async Task<Dictionary<string, object>> GetStatsAsync()
        {
            var totalDocs = await GetDocumentCountAsync();
            return new Dictionary<string, object>
            {
                ["provider"] = "MongoDB",
                ["total_documents"] = totalDocs,
                ["health"] = await IsHealthyAsync(),
                ["last_check"] = DateTime.UtcNow
            };
        }

        private double CalcularSimilaridadeCoseno(double[] embedding1, double[] embedding2)
        {
            double dotProduct = 0.0;
            double normA = 0.0;
            double normB = 0.0;
            for (int i = 0; i < embedding1.Length; i++)
            {
                dotProduct += embedding1[i] * embedding2[i];
                normA += Math.Pow(embedding1[i], 2);
                normB += Math.Pow(embedding2[i], 2);
            }
            return dotProduct / (Math.Sqrt(normA) * Math.Sqrt(normB));
        }
    }
}
#pragma warning restore SKEXP0001 // Type is for evaluation purposes only and is subject to change or removal in future updates. Suppress this diagnostic to proceed.