feat: 新增向量嵌入服务支持

新增SiliconFlow向量嵌入服务实现，支持文本向量化功能：
- 新增ITextEmbeddingService接口和SiliconFlowTextEmbeddingService实现
- 新增EmbeddingCreateRequest/Response等向量相关DTO
- 在AiGateWayManager中新增EmbeddingForStatisticsAsync方法
- 在OpenApiService中新增向量生成API接口
- 扩展ModelTypeEnum枚举支持Embedding类型
- 优化ThorChatMessage的Content属性处理逻辑

Yi.Abp.Net8/module/ai-hub/Yi.Framework.AiHub.Domain/Managers/AiGateWayManager.cs

@@ -2,6 +2,7 @@ using System.Collections.Concurrent;
 using System.Diagnostics;
 using System.Runtime.CompilerServices;
 using System.Text;
+using System.Text.Json;
 using Microsoft.AspNetCore.Http;
 using Microsoft.Extensions.DependencyInjection;
 using Microsoft.Extensions.Logging;
@@ -10,6 +11,7 @@ using Newtonsoft.Json.Serialization;
 using Volo.Abp.Domain.Services;
 using Yi.Framework.AiHub.Application.Contracts.Dtos;
 using Yi.Framework.AiHub.Application.Contracts.Dtos.OpenAi;
+using Yi.Framework.AiHub.Application.Contracts.Dtos.OpenAi.Embeddings;
 using Yi.Framework.AiHub.Application.Contracts.Dtos.OpenAi.Images;
 using Yi.Framework.AiHub.Domain.AiGateWay;
 using Yi.Framework.AiHub.Domain.AiGateWay.Exceptions;
@@ -276,19 +278,20 @@ public class AiGateWayManager : DomainService
     /// <param name="request"></param>
     /// <exception cref="BusinessException"></exception>
     /// <exception cref="Exception"></exception>
-    public async Task CreateImageForStatisticsAsync(HttpContext context,Guid? userId,Guid? sessionId, ImageCreateRequest request)
+    public async Task CreateImageForStatisticsAsync(HttpContext context, Guid? userId, Guid? sessionId,
+        ImageCreateRequest request)
     {
         try
         {
             var model = request.Model;
             if (string.IsNullOrEmpty(model)) model = "dall-e-2";
-            
+
             var modelDescribe = await GetModelAsync(model);
-            
+
             // 获取渠道指定的实现类型的服务
             var imageService =
                 LazyServiceProvider.GetRequiredKeyedService<IImageService>(modelDescribe.HandlerName);
-            
+
             var response = await imageService.CreateImage(request, modelDescribe);
 
             if (response.Error != null || response.Results.Count == 0)
@@ -297,7 +300,7 @@ public class AiGateWayManager : DomainService
             }
 
             await context.Response.WriteAsJsonAsync(response);
-            
+
             await _aiMessageManager.CreateUserMessageAsync(userId, sessionId,
                 new MessageInputDto
                 {
@@ -322,4 +325,110 @@ public class AiGateWayManager : DomainService
             throw new UserFriendlyException(errorContent);
         }
     }
+
+
+    /// <summary>
+    /// 向量生成
+    /// </summary>
+    /// <param name="context"></param>
+    /// <param name="sessionId"></param>
+    /// <param name="input"></param>
+    /// <param name="userId"></param>
+    /// <exception cref="Exception"></exception>
+    /// <exception cref="BusinessException"></exception>
+    public async Task EmbeddingForStatisticsAsync(HttpContext context, Guid? userId, Guid? sessionId,
+        ThorEmbeddingInput input)
+    {
+        try
+        {
+            if (input == null) throw new Exception("模型校验异常");
+
+            using var embedding =
+                Activity.Current?.Source.StartActivity("向量模型调用");
+
+            var modelDescribe = await GetModelAsync(input.Model);
+
+            // 获取渠道指定的实现类型的服务
+            var embeddingService =
+                LazyServiceProvider.GetRequiredKeyedService<ITextEmbeddingService>(modelDescribe.HandlerName);
+
+            var embeddingCreateRequest = new EmbeddingCreateRequest
+            {
+                Model = input.Model,
+                EncodingFormat = input.EncodingFormat
+            };
+
+            //dto进行转换，支持多种格式
+            if (input.Input is JsonElement str)
+            {
+                if (str.ValueKind == JsonValueKind.Array)
+                {
+                    var inputString = str.EnumerateArray().Select(x => x.ToString()).ToArray();
+                    embeddingCreateRequest.InputAsList = inputString.ToList();
+                }
+                else
+                {
+                    throw new Exception("Input，输入格式错误，非string或Array类型");
+                }
+            }
+            else if (input.Input is string strInput)
+            {
+                embeddingCreateRequest.Input = strInput;
+            }
+            else
+            {
+                throw new Exception("Input，输入格式错误，未找到类型");
+            }
+
+
+            var stream =
+                await embeddingService.EmbeddingAsync(embeddingCreateRequest, modelDescribe, context.RequestAborted);
+
+            var usage = new ThorUsageResponse()
+            {
+                InputTokens = stream.Usage?.InputTokens ?? 0,
+                CompletionTokens = 0,
+                TotalTokens = stream.Usage?.InputTokens ?? 0
+            };
+            await context.Response.WriteAsJsonAsync(new
+            {
+                input.Model,
+                stream.Data,
+                stream.Error,
+                stream.ObjectTypeName,
+                Usage = usage
+            });
+
+            await _aiMessageManager.CreateUserMessageAsync(userId, sessionId,
+                new MessageInputDto
+                {
+                    Content = string.Empty,
+                    ModelId = input.Model,
+                    TokenUsage = usage,
+                });
+
+            await _aiMessageManager.CreateSystemMessageAsync(userId, sessionId,
+                new MessageInputDto
+                {
+                    Content = string.Empty,
+                    ModelId = input.Model,
+                    TokenUsage = usage
+                });
+
+            await _usageStatisticsManager.SetUsageAsync(userId, input.Model, usage);
+        }
+        catch (ThorRateLimitException)
+        {
+            context.Response.StatusCode = 429;
+        }
+        catch (UnauthorizedAccessException e)
+        {
+            context.Response.StatusCode = 401;
+        }
+        catch (Exception e)
+        {
+            var errorContent = $"嵌入Ai异常，异常信息：\n当前Ai模型：{input.Model}\n异常信息：{e.Message}\n异常堆栈:{e}";
+            throw new UserFriendlyException(errorContent);
+        }
+    }
 }