100 lines
4.2 KiB
C#
100 lines
4.2 KiB
C#
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using ChatApi.Models;
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using Microsoft.SemanticKernel;
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using Microsoft.SemanticKernel.ChatCompletion;
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using Microsoft.SemanticKernel.Embeddings;
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using OllamaSharp.Models.Chat;
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#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.
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namespace ChatApi.Services.ResponseService
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{
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public class ResponseCompanyService : IResponseService
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{
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private readonly ChatHistoryService _chatHistoryService;
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private readonly Kernel _kernel;
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private readonly TextFilter _textFilter;
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private readonly SharepointDomvsService _sharepointDomvsService;
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private readonly IChatCompletionService _chatCompletionService;
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public ResponseCompanyService(
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ChatHistoryService chatHistoryService,
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Kernel kernel,
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TextFilter textFilter,
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SharepointDomvsService sharepointDomvsService,
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IChatCompletionService chatCompletionService)
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{
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this._chatHistoryService = chatHistoryService;
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this._kernel = kernel;
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this._textFilter = textFilter;
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this._sharepointDomvsService = sharepointDomvsService;
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this._chatCompletionService = chatCompletionService;
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}
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public EnumClassification Classification => EnumClassification.Company;
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public async Task<string> GetResponse(HttpContext context, UserData userData, string sessionId, string question)
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{
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var stopWatch = new System.Diagnostics.Stopwatch();
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stopWatch.Start();
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SessionIdStore sessionIdStore = new SessionIdStore(context);
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var resposta = await BuscarTextoRelacionado(question);
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question = "Para responder à pergunta: \"" + question + "\" por favor, gere um resumo com 3 linhas baseado exclusivamente no texto: \"" + resposta + "\"";
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ChatHistory history = _chatHistoryService.GetSumarizer(sessionId);
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history.AddUserMessage(question);
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var response = await _chatCompletionService.GetChatMessageContentAsync(history);
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history.AddMessage(response.Role, response.Content ?? "");
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_chatHistoryService.UpdateHistory(sessionId, history);
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stopWatch.Stop();
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return $"{response.Content ?? ""}\n\nTempo: {stopWatch.ElapsedMilliseconds / 1000}s";
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}
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async Task<string> BuscarTextoRelacionado(string pergunta)
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{
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var embeddingService = _kernel.GetRequiredService<ITextEmbeddingGenerationService>();
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var embeddingPergunta = await embeddingService.GenerateEmbeddingAsync(_textFilter.ToLowerAndWithoutAccents(pergunta));
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var embeddingArrayPergunta = embeddingPergunta.ToArray().Select(e => (double)e).ToArray();
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var textos = await _sharepointDomvsService.GetAsync();
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TextoComEmbedding melhorTexto = null;
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double melhorSimilaridade = -1.0;
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foreach (var texto in textos)
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{
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double similaridade = CalcularSimilaridadeCoseno(embeddingArrayPergunta, texto.Embedding);
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if (similaridade > melhorSimilaridade)
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{
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melhorSimilaridade = similaridade;
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melhorTexto = texto;
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}
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}
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// 4. Retornar o conteúdo mais similar, ou uma mensagem padrão
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return melhorTexto != null ? melhorTexto.Conteudo : "Não encontrei uma resposta adequada.";
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}
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double CalcularSimilaridadeCoseno(double[] embedding1, double[] embedding2)
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{
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double dotProduct = 0.0;
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double normA = 0.0;
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double normB = 0.0;
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for (int i = 0; i < embedding1.Length; i++)
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{
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dotProduct += embedding1[i] * embedding2[i];
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normA += Math.Pow(embedding1[i], 2);
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normB += Math.Pow(embedding2[i], 2);
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}
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return dotProduct / (Math.Sqrt(normA) * Math.Sqrt(normB));
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}
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}
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}
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#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.
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