style: 调整架构引用
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chenchun
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using System.ComponentModel.DataAnnotations;
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using System.Text.Json.Serialization;
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namespace Yi.Framework.AiHub.Domain.Shared.Dtos.OpenAi.Embeddings;
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//TODO add model validation
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//TODO check what is string or array for prompt,..
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public record EmbeddingCreateRequest
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{
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/// <summary>
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/// Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs
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/// in a single request, pass an array of strings or array of token arrays. Each input must not exceed 2048 tokens in
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/// length.
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/// Unless your are embedding code, we suggest replacing newlines (`\n`) in your input with a single space, as we have
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/// observed inferior results when newlines are present.
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/// </summary>
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/// <see href="https://platform.openai.com/docs/api-reference/embeddings/create#embeddings/create-input" />
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[JsonIgnore]
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public List<string>? InputAsList { get; set; }
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/// <summary>
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/// Input text to get embeddings for, encoded as a string or array of tokens. To get embeddings for multiple inputs
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/// in a single request, pass an array of strings or array of token arrays. Each input must not exceed 2048 tokens in
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/// length.
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/// Unless your are embedding code, we suggest replacing newlines (`\n`) in your input with a single space, as we have
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/// observed inferior results when newlines are present.
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/// </summary>
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/// <see href="https://platform.openai.com/docs/api-reference/embeddings/create#embeddings/create-input" />
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[JsonIgnore]
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public string? Input { get; set; }
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[JsonPropertyName("input")]
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public IList<string>? InputCalculated
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{
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get
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{
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if (Input != null && InputAsList != null)
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{
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throw new ValidationException(
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"Input and InputAsList can not be assigned at the same time. One of them is should be null.");
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}
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if (Input != null)
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{
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return new List<string> { Input };
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}
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return InputAsList;
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}
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}
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/// <summary>
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/// ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your
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/// available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
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/// </summary>
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/// <see href="https://platform.openai.com/docs/api-reference/embeddings/create#embeddings/create-model" />
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[JsonPropertyName("model")]
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public string? Model { get; set; }
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/// <summary>
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/// The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
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/// </summary>
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/// <see href="https://platform.openai.com/docs/api-reference/embeddings/create#embeddings-create-dimensions" />
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[JsonPropertyName("dimensions")]
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public int? Dimensions { get; set; }
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/// <summary>
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/// The format to return the embeddings in. Can be either float or base64.
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/// </summary>
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/// <returns></returns>
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[JsonPropertyName("encoding_format")]
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public string? EncodingFormat { get; set; }
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public IEnumerable<ValidationResult> Validate()
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{
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throw new NotImplementedException();
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}
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}
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@@ -0,0 +1,111 @@
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using System.Buffers;
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using System.Text.Json;
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using System.Text.Json.Serialization;
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namespace Yi.Framework.AiHub.Domain.Shared.Dtos.OpenAi.Embeddings;
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public record EmbeddingCreateResponse : ThorBaseResponse
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{
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[JsonPropertyName("model")] public string Model { get; set; }
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[JsonPropertyName("data")] public List<EmbeddingResponse> Data { get; set; } = [];
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/// <summary>
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/// 类型转换，如果类型是base64,则将float[]转换为base64,如果是空或是float和原始类型一样，则不转换
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/// </summary>
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public void ConvertEmbeddingData(string? encodingFormat)
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{
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if (Data.Count == 0)
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{
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return;
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}
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switch (encodingFormat)
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{
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// 判断第一个是否是float[]，如果是则不转换
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case null or "float" when Data[0].Embedding is float[]:
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return;
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// 否则转换成float[]
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case null or "float":
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{
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foreach (var embeddingResponse in Data)
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{
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if (embeddingResponse.Embedding is string base64)
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{
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embeddingResponse.Embedding = Convert.FromBase64String(base64);
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}
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}
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return;
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}
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// 判断第一个是否是string，如果是则不转换
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case "base64" when Data[0].Embedding is string:
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return;
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// 否则转换成base64
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case "base64":
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{
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foreach (var embeddingResponse in Data)
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{
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if (embeddingResponse.Embedding is JsonElement str)
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{
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if (str.ValueKind == JsonValueKind.Array)
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{
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var floats = str.EnumerateArray().Select(element => element.GetSingle()).ToArray();
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embeddingResponse.Embedding = ConvertFloatArrayToBase64(floats);
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}
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}
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else if (embeddingResponse.Embedding is IList<double> doubles)
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{
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embeddingResponse.Embedding = ConvertFloatArrayToBase64(doubles.ToArray());
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}
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}
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break;
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}
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}
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}
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public static string ConvertFloatArrayToBase64(double[] floatArray)
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{
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// 将 float[] 转换成 byte[]
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byte[] byteArray = ArrayPool<byte>.Shared.Rent(floatArray.Length * sizeof(float));
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try
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{
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Buffer.BlockCopy(floatArray, 0, byteArray, 0, byteArray.Length);
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// 将 byte[] 转换成 base64 字符串
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return Convert.ToBase64String(byteArray);
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}
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finally
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{
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ArrayPool<byte>.Shared.Return(byteArray);
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}
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}
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public static string ConvertFloatArrayToBase64(float[] floatArray)
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{
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// 将 float[] 转换成 byte[]
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byte[] byteArray = ArrayPool<byte>.Shared.Rent(floatArray.Length * sizeof(float));
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try
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{
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Buffer.BlockCopy(floatArray, 0, byteArray, 0, floatArray.Length);
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// 将 byte[] 转换成 base64 字符串
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return Convert.ToBase64String(byteArray);
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}
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finally
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{
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ArrayPool<byte>.Shared.Return(byteArray);
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}
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}
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[JsonPropertyName("usage")] public ThorUsageResponse? Usage { get; set; }
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}
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public record EmbeddingResponse
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{
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[JsonPropertyName("index")] public int? Index { get; set; }
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[JsonPropertyName("embedding")] public object Embedding { get; set; }
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}
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@@ -0,0 +1,22 @@
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using System.Text.Json.Serialization;
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namespace Yi.Framework.AiHub.Domain.Shared.Dtos.OpenAi.Embeddings;
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public sealed class ThorEmbeddingInput
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{
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[JsonPropertyName("model")]
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public string Model { get; set; }
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[JsonPropertyName("input")]
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public object Input { get; set; }
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[JsonPropertyName("encoding_format")]
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public string EncodingFormat { get; set; }
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[JsonPropertyName("dimensions")]
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public int? Dimensions { get; set; }
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[JsonPropertyName("user")]
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public string? User { get; set; }
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}
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