build-flow.api/tests/BuildFlow.Rag.Tests/AiModelRuntimeConfigResolverTests.cs

using BuildFlow.Domain.Entities.AiModel;
using BuildFlow.Domain.Entities.ChatMessage;
using BuildFlow.Domain.Entities.Conversation;
using BuildFlow.Domain.Entities.Document;
using BuildFlow.Domain.Entities.DocumentChunk;
using BuildFlow.Domain.Entities.KnowledgeBase;
using BuildFlow.Domain.Enums;
using BuildFlow.Infrastructure.AI;
using BuildFlow.Infrastructure.Persistence;
using Microsoft.EntityFrameworkCore;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Options;
using Xunit;

namespace BuildFlow.Rag.Tests;

/// <summary>
/// AI 运行时模型配置解析测试。确保 Chat/Embedding 调用优先使用模型管理里配置的默认本地模型。
/// </summary>
public class AiModelRuntimeConfigResolverTests
{
    [Fact]
    public async Task ResolveAsyncShouldReplaceLegacyRagOnlyChatInstructions()
    {
        var services = new ServiceCollection();
        var dbName = $"rag-chat-model-{Guid.NewGuid():N}";
        services.AddDbContext<RagDbContext>(options =>
            options.UseInMemoryDatabase(dbName));
        services.AddScoped<AiModelRuntimeConfigResolver>();
        services.AddSingleton(Options.Create(new AiOptions
        {
            BaseUrl = "http://localhost:1234/v1",
            ApiKey = "lm-studio",
            ChatModel = "fallback-chat",
            EmbeddingModel = "fallback-embedding",
            DefaultInstructions = "你是 BuildFlow AI 助手。没有知识库上下文时按普通对话直接回答。",
        }));

        await using var provider = services.BuildServiceProvider();
        await using (var scope = provider.CreateAsyncScope())
        {
            var db = scope.ServiceProvider.GetRequiredService<RagDbContext>();
            await db.AiModels.AddAsync(new AiModelEntity
            {
                Id = Guid.NewGuid(),
                Name = "旧对话模型",
                Provider = ModelProviderEnum.LMStudio,
                ModelId = "qwen/qwen3.6-27b",
                BaseUrl = "http://localhost:1234/v1",
                ApiKey = "lm-studio",
                ModelType = ModelTypeEnum.Chat,
                IsActive = true,
                IsDefault = true,
                DefaultInstructions = "你是一个RAG知识库助手，基于提供的上下文准确回答用户问题。如果上下文中没有相关信息，请如实说明。",
                CreatedBy = "test",
                UpdatedBy = "test",
                OperatorIP = "127.0.0.1",
            });
            await db.SaveChangesAsync();
        }

        await using (var scope = provider.CreateAsyncScope())
        {
            var resolver = scope.ServiceProvider.GetRequiredService<AiModelRuntimeConfigResolver>();

            var config = await resolver.ResolveAsync(ModelTypeEnum.Chat, CancellationToken.None);

            Assert.Equal("你是 BuildFlow AI 助手。没有知识库上下文时按普通对话直接回答。", config.DefaultInstructions);
        }
    }

    [Fact]
    public async Task ResolveAsyncShouldPreferDefaultEmbeddingModelFromDatabase()
    {
        var services = new ServiceCollection();
        var dbName = $"rag-ai-model-{Guid.NewGuid():N}";
        services.AddDbContext<RagDbContext>(options =>
            options.UseInMemoryDatabase(dbName));
        services.AddScoped<AiModelRuntimeConfigResolver>();
        services.AddSingleton(Options.Create(new AiOptions
        {
            BaseUrl = "https://fallback.example/v1",
            ApiKey = "fallback-key",
            ChatModel = "fallback-chat",
            EmbeddingModel = "fallback-embedding",
            DefaultInstructions = "fallback instructions",
        }));

        await using var provider = services.BuildServiceProvider();
        await using (var scope = provider.CreateAsyncScope())
        {
            var db = scope.ServiceProvider.GetRequiredService<RagDbContext>();
            await db.AiModels.AddRangeAsync(
                new AiModelEntity
                {
                    Id = Guid.NewGuid(),
                    Name = "旧嵌入模型",
                    Provider = ModelProviderEnum.OpenAI,
                    ModelId = "remote-embedding",
                    BaseUrl = "https://remote.example/v1",
                    ApiKey = "remote-key",
                    ModelType = ModelTypeEnum.Embedding,
                    IsActive = true,
                    IsDefault = false,
                    CreatedBy = "test",
                    UpdatedBy = "test",
                    OperatorIP = "127.0.0.1",
                },
                new AiModelEntity
                {
                    Id = Guid.NewGuid(),
                    Name = "本地嵌入模型",
                    Provider = ModelProviderEnum.LMStudio,
                    ModelId = "text-embedding-embeddinggemma-300m",
                    BaseUrl = "http://localhost:1234/v1",
                    ApiKey = "lm-studio",
                    ModelType = ModelTypeEnum.Embedding,
                    IsActive = true,
                    IsDefault = true,
                    CreatedBy = "test",
                    UpdatedBy = "test",
                    OperatorIP = "127.0.0.1",
                });
            await db.SaveChangesAsync();
        }

        await using (var scope = provider.CreateAsyncScope())
        {
            var resolver = scope.ServiceProvider.GetRequiredService<AiModelRuntimeConfigResolver>();

            var config = await resolver.ResolveAsync(ModelTypeEnum.Embedding, CancellationToken.None);

            Assert.Equal("text-embedding-embeddinggemma-300m", config.ModelId);
            Assert.Equal("http://localhost:1234/v1", config.BaseUrl);
            Assert.Equal("lm-studio", config.ApiKey);
        }
    }
}