67b030c3c5
- AI chat with SSE streaming (Microsoft Agent Framework + Qwen) - RAG Q&A with hybrid retrieval (vector + keyword RRF fusion) - Knowledge base CRUD with semantic text chunking - Embedding generation via Azure.AI.OpenAI / LM Studio - Document upload with chunked upload support - Redis caching for chat messages - Chunk/vector preview endpoints - gRPC auth service improvements - Removed demo menus, cleaned up seed data
19 lines
718 B
C#
19 lines
718 B
C#
using MediatR;
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using RAG.Application.Embedding.DTOs;
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using RAG.Domain.Interfaces;
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namespace RAG.Application.Embedding.Commands;
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public record EmbedBatchCommand(List<string> Texts) : IRequest<EmbeddingBatchResponse>;
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public class EmbedBatchCommandHandler(IEmbeddingService embeddingService)
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: IRequestHandler<EmbedBatchCommand, EmbeddingBatchResponse>
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{
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public async Task<EmbeddingBatchResponse> Handle(EmbedBatchCommand request, CancellationToken ct)
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{
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var vectors = await embeddingService.EmbedBatchAsync(request.Texts, ct);
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var dimensions = vectors.FirstOrDefault()?.Length ?? 0;
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return new EmbeddingBatchResponse(vectors.Select(v => v.ToList()).ToList(), dimensions);
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}
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}
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