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using System.Threading.Tasks;
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using OllamaSharp;
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using OllamaSharp.Models;
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using OllamaSharp.Models.Chat;
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using Xunit.Abstractions;
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namespace OllamaStudy.UseOllamaSharp
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{
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public class OllamaApiTest
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{
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private readonly ITestOutputHelper _output;
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private readonly IOptionsMonitor<OllamaServerOption> _optionsMonitor;
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private readonly IConfiguration _configuration;
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private readonly IOllamaApiClient _ollamaApiClient;
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public OllamaApiTest
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(
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ITestOutputHelper outputHelper,
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IOptionsMonitor<OllamaServerOption> optionsMonitor,
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IConfiguration configuration,
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IOllamaApiClient ollamaApiClient
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)
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{
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_output = outputHelper;
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_optionsMonitor = optionsMonitor;
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_configuration = configuration;
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_ollamaApiClient = ollamaApiClient;
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}
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#region 配置与ID
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[Fact]
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public void Config_Test()
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{
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var ollamaServerOption = new OllamaServerOption();
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_configuration.GetSection("OllamaServer").Bind(ollamaServerOption);
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Assert.NotNull(ollamaServerOption);
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Assert.NotNull(ollamaServerOption.OllamaServerUrl);
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Assert.NotNull(ollamaServerOption.Model);
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}
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[Fact]
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public void OllamaOption_Test()
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{
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var options = _optionsMonitor.CurrentValue;
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Assert.NotNull(options);
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Assert.NotNull(options.Model);
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Assert.NotNull(options.OllamaServerUrl);
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}
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#endregion
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#region 生成补全
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/// <summary>
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/// 流式生成请求
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/// </summary>
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[Fact]
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public async Task Completion_Request_Streaming_Test()
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{
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var generateRequest = new GenerateRequest()
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{
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Prompt = """天空为什么是蓝色的？""",
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//Stream = false,
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};
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IAsyncEnumerable<GenerateResponseStream?>? response = _ollamaApiClient.GenerateAsync(generateRequest);
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StringBuilder stringBuilder = new StringBuilder();
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await foreach (GenerateResponseStream? stream in response)
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{
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stringBuilder.Append(stream?.Response);
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}
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_output.WriteLine(stringBuilder.ToString());
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}
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/// <summary>
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/// 流式生成请求
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/// </summary>
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[Fact]
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public async Task Completion_Request_NoStreaming_Test()
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{
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var generateRequest = new GenerateRequest()
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{
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Prompt = """天空为什么是蓝色的？""",
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Stream = false,
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};
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var responseStream = await _ollamaApiClient.GenerateAsync(generateRequest).StreamToEndAsync();
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//处理响应
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var responseText = responseStream?.Response;
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Assert.NotNull(responseText);
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_output.WriteLine(responseText);
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}
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/// <summary>
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/// 带后缀的生成请求
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/// </summary>
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/// <returns></returns>
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[Fact]
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public async Task Completion_Request_WithSuffix_Test()
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{
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var generateRequest = new GenerateRequest()
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{
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Model = ModelSelecter.ModelWithSuffixAndImage,
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Prompt = """def compute_gcd(a, b):""",
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//（可选）模型响应后的文本
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Suffix = " return result",
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Options = new RequestOptions()
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{
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Temperature = 0,
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},
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Stream = false,
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};
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var responseStream = await _ollamaApiClient.GenerateAsync(generateRequest).StreamToEndAsync();
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//处理响应
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Assert.NotNull(responseStream);
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Assert.NotNull(responseStream.Response);
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_output.WriteLine(responseStream?.Response);
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}
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/// <summary>
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/// 结构化输出请求
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/// </summary>
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[Fact]
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public async Task completion_request_structuredoutputs_test()
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{
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var generateRequest = new GenerateRequest()
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{
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Prompt = "22岁的 ollama 正忙于拯救世界。使用json响应。",
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Format = new
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{
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type = "object",
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properties = new { age = new { type = "integer" }, available = new { type = "boolean" } },
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required = new string[] { "age", "available" }
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},
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Options = new RequestOptions()
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{
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Temperature = 0,
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},
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Stream = false,
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};
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//处理响应
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GenerateDoneResponseStream? responseStream = await _ollamaApiClient.GenerateAsync(generateRequest).StreamToEndAsync();
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Assert.NotNull(responseStream);
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Assert.NotNull(responseStream.Response);
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Assert.True(responseStream.Done);
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_output.WriteLine(responseStream.Response);
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}
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/// <summary>
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/// json模式请求
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/// </summary>
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[Fact]
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public async Task Completion_Request_JsonMode_Test()
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{
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var generateRequest = new GenerateRequest()
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{
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Prompt = "一天中不同时间的天空是什么颜色的？使用JSON进行响应!",
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Format = "json",
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Options = new RequestOptions()
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{
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Temperature = 0,
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},
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Stream = false,
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};
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//处理响应
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GenerateDoneResponseStream? responseStream = await _ollamaApiClient.GenerateAsync(generateRequest).StreamToEndAsync();
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var responseObject = JsonSerializer.Deserialize<object>(responseStream?.Response ?? "{}");
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Assert.NotNull(responseStream);
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Assert.NotNull(responseStream.Response);
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Assert.True(responseStream.Done);
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_output.WriteLine(responseStream.Response);
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}
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/// <summary>
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/// 带图像的生成请求
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/// </summary>
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[Fact]
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public async Task Completion_Request_WhithImages_Test()
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{
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var generateRequest = new GenerateRequest()
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{
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// (必需) 模型名称
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Model = ModelSelecter.ModelWithSuffixAndImage,
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// (必需) 生成响应的提示词
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Prompt = "这张照片里有什么东西？",
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//（可选）Base64 编码的图像列表（适用于 LLAVA 等多模态模型）
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Images = new string[]
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{
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"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"
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},
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//（可选）流式处理响应： 可通过设置 false 来禁用流式处理
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Stream = false,
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//（可选）控制模型在请求后加载到内存中的时间（默认值：5M）
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KeepAlive = "5m",
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};
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//处理响应
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var response = await _ollamaApiClient.GenerateAsync(generateRequest).StreamToEndAsync();
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_output.WriteLine(response?.Response);
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Assert.NotNull(response);
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Assert.NotEmpty(response.Response);
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}
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/// <summary>
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/// 原始模式请求
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/// 在某些情况下，您可能希望绕过模板系统并提供完整的提示。在这种情况下，您可以使用raw参数禁用模板。还要注意，原始模式不会返回上下文。
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/// </summary>
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/// <returns></returns>
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[Fact]
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public async Task Completion_Request_RawMode_Test()
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{
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var generateRequest = new GenerateRequest()
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{
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Model = ModelSelecter.ModelWithRawmodel,
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Prompt = """[INST] 天空为什么是蓝色的? [/INST]""",
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Stream = false,
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Raw = true,
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};
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//处理响应
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var response = await _ollamaApiClient.GenerateAsync(generateRequest).StreamToEndAsync();
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_output.WriteLine(response?.Response);
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Assert.NotNull(response);
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Assert.NotEmpty(response.Response);
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}
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/// <summary>
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/// 可复现输出的请求
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/// </summary>
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/// <returns></returns>
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|
[Fact]
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|
public async Task Completion_Request_ReproducibleOutputs_Test()
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|
{
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//第一次请求
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var generateRequest1 = new GenerateRequest()
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{
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|
|
Prompt = """天空为什么是蓝色的? """,
|
|
|
Stream = false,
|
|
|
Options = new RequestOptions()
|
|
|
{
|
|
|
Seed = 999999999,
|
|
|
Temperature = 0,
|
|
|
}
|
|
|
};
|
|
|
|
|
|
//处理响应
|
|
|
var response1 = await _ollamaApiClient.GenerateAsync(generateRequest1).StreamToEndAsync();
|
|
|
_output.WriteLine(response1?.Response);
|
|
|
|
|
|
var response1Text = response1?.Response;
|
|
|
|
|
|
//第二次请求
|
|
|
var generateRequest2 = new GenerateRequest()
|
|
|
{
|
|
|
Prompt = """天空为什么是蓝色的? """,
|
|
|
Stream = false,
|
|
|
Options = new RequestOptions()
|
|
|
{
|
|
|
Seed = 999999999,
|
|
|
Temperature = 0,
|
|
|
}
|
|
|
};
|
|
|
|
|
|
//处理响应
|
|
|
var response2 = await _ollamaApiClient.GenerateAsync(generateRequest2).StreamToEndAsync();
|
|
|
_output.WriteLine(response2?.Response);
|
|
|
|
|
|
var response2Text = response2?.Response;
|
|
|
|
|
|
Assert.Equal(response1Text, response2Text);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 参数化生成请求
|
|
|
/// </summary>
|
|
|
[Fact]
|
|
|
public async Task Completion_Request_Options_Test()
|
|
|
{
|
|
|
//使用提供的模型为给定的提示生成响应。这是一个流式端点，因此会有一系列响应。最终的响应对象将包括请求中的统计数据和其他数据。
|
|
|
var generateRequest = new GenerateRequest()
|
|
|
{
|
|
|
/*基础选项*/
|
|
|
|
|
|
// (必需) 模型名称
|
|
|
Model = ModelSelecter.ModelWithSuffixAndImage,
|
|
|
|
|
|
// (必需) 生成响应的提示词
|
|
|
Prompt = "天空为什么是蓝色的？",
|
|
|
|
|
|
//（可选）模型响应后的文本:只有专用模型才支持(qwen2.5-coder:3b等), 模型不支持则异常
|
|
|
//Suffix for Fill-In-the-Middle generate
|
|
|
//Suffix = " return result",
|
|
|
|
|
|
//（可选）Base64 编码的图像列表（适用于 LLAVA 等多模态模型）
|
|
|
//Images = new string[]
|
|
|
//{
|
|
|
// "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",
|
|
|
|
|
|
// "base64"
|
|
|
//},
|
|
|
|
|
|
/*高级选项*/
|
|
|
|
|
|
//（可选）返回响应的格式：json 或 JSON schema
|
|
|
//Format = "json",
|
|
|
|
|
|
//选项参数
|
|
|
Options = new RequestOptions()
|
|
|
{
|
|
|
//设置模型回溯多长时间以防止重复(0 = 禁用，-1 = num_ctx)
|
|
|
RepeatLastN = 60,
|
|
|
|
|
|
//设置对重复项的惩罚力度(越低越宽松):较高的值（例如1.5）将更严厉地惩罚重复，而较低的值（如0.9）将更宽容。（默认值：1.1）
|
|
|
RepeatPenalty = 1.1f,
|
|
|
|
|
|
//模型的温度:值越大，模型的答案越具有创造性。（默认值：0.8）
|
|
|
Temperature = 0.8f,
|
|
|
|
|
|
//设置用于生成的随机数种子。将其设置为特定数字将使模型为同一提示生成相同的文本。（默认值：0）
|
|
|
Seed = 0,
|
|
|
|
|
|
//设置要使用的停止序列。遇到此模式时，LLM 将停止生成文本并返回。通过在模型文件中指定多个单独的停止参数，可以设置多个停止模式
|
|
|
Stop = new string[] { "exc", "stop" },
|
|
|
|
|
|
//生成文本时要预测的最大令牌数。（默认值：-1，无限生成）
|
|
|
NumPredict = -1,
|
|
|
|
|
|
//降低产生无意义的可能性。较高的值（例如 100）将给出更多样化的答案，而较低的值（例如 10）将更保守。（默认值：40）
|
|
|
TopK = 40,
|
|
|
|
|
|
//与 top-k 一起使用。较高的值（例如 0.95）将导致文本更加多样化，而较低的值（例如 0.5）将生成更集中和保守的文本。（默认值：0.9）
|
|
|
TopP = 0.9f,
|
|
|
|
|
|
//启用F16键值对 (默认值：false)
|
|
|
F16kv = false,
|
|
|
|
|
|
//根据令牌在提示中的出现频率对其进行处罚
|
|
|
FrequencyPenalty = 0.0f,
|
|
|
|
|
|
//返回所有令牌的logits，而不仅仅是最后一个。（默认值：False）
|
|
|
LogitsAll = false,
|
|
|
|
|
|
//启用低VRAM模式（默认值：False）
|
|
|
LowVram = false,
|
|
|
|
|
|
//此选项控制哪个GPU用于小张量。在所有GPU上分割计算的开销是不值得的。GPU将使用稍多的VRAM来存储临时结果的暂存缓冲区。默认情况下，使用GPU 0。
|
|
|
MainGpu = null,
|
|
|
|
|
|
//top_p的替代方案，旨在确保质量和多样性的平衡。min_p表示相对于最有可能的令牌的概率，考虑令牌的最小概率。例如，当min_p=0.05并且最可能的令牌具有0.9的概率时，值小于0.05*0.9=0.045的logits被过滤掉。（默认值：0.0）
|
|
|
MinP = 0.0f,
|
|
|
|
|
|
//启用Mirostat采样以控制困惑。（默认值：0，0=禁用，1=Mirostat，2=Mirostat2.0）
|
|
|
MiroStat = 0,
|
|
|
|
|
|
//影响算法对生成文本反馈的响应速度。较低的学习率将导致较慢的调整，而较高的学习率会使算法更具响应性。（默认值：0.1）
|
|
|
MiroStatEta = 0.1f,
|
|
|
|
|
|
//控制输出的连贯性和多样性之间的平衡。较低的值将导致文本更加集中和连贯。（默认值：5.0）
|
|
|
MiroStatTau = 5.0f,
|
|
|
|
|
|
//设置用于生成下一个标记的上下文窗口的大小
|
|
|
NumCtx = 2048,
|
|
|
|
|
|
//启用NUMA支持。（默认值：False）
|
|
|
Numa = false,
|
|
|
|
|
|
//提示处理最大批量。（默认值：512）
|
|
|
NumBatch = 512,
|
|
|
|
|
|
//要发送到GPU的层数。在macOS上，默认为1启用金属支持，0禁用。
|
|
|
NumGpu = 0,
|
|
|
|
|
|
//变压器层中GQA组的数量。某些型号需要，例如骆驼2:70b为8
|
|
|
NumGqa = null,
|
|
|
|
|
|
//从初始提示开始保留的令牌数。（默认值：4，-1=全部）
|
|
|
NumKeep = 4,
|
|
|
|
|
|
//设置计算期间要使用的线程数。默认情况下，Ollama会检测到这一点以获得最佳性能。建议将此值设置为系统具有的物理CPU核数（而不是逻辑核数）。
|
|
|
//NumThread = null,
|
|
|
|
|
|
//惩罚换行符（默认值：True）
|
|
|
PenalizeNewline = true,
|
|
|
|
|
|
//根据令牌在提示中的存在对其进行处罚。（默认值：0.0）
|
|
|
PresencePenalty = 0.0f,
|
|
|
|
|
|
//无尾采样用于减少输出中不太可能的令牌的影响。更高的值（例如2.0）将更多地减少影响，而1.0的值将禁用此设置。（默认值：1）
|
|
|
TfsZ = 1,
|
|
|
|
|
|
//用于采样的典型p值。论文中描述的本地典型采样实现https://arxiv.org/abs/2202.00666.（默认值：1.0）
|
|
|
TypicalP = 1.0f,
|
|
|
|
|
|
//将模型锁定在内存中以防止交换。这可以提高性能，但它会使用更多的RAM，并可能减慢加载速度。（默认值：False）
|
|
|
UseMlock = false,
|
|
|
|
|
|
//默认情况下，模型被映射到内存中，这允许系统根据需要仅加载必要的部分。禁用mmap会使加载速度变慢，但如果您没有使用mlock，则可以减少分页。如果模型大于RAM，关闭mmap会阻止其加载。
|
|
|
//默认值：true）
|
|
|
UseMmap = true,
|
|
|
|
|
|
//只加载词汇，不加载权重。（默认值：False）
|
|
|
VocabOnly = false,
|
|
|
},
|
|
|
|
|
|
//（可选）流式处理响应： 可通过设置 false 来禁用流式处理
|
|
|
Stream = true,
|
|
|
|
|
|
//（可选）系统消息（覆盖 Modelfile 中定义的内容）
|
|
|
//System = "Modelfile 文件格式",
|
|
|
|
|
|
//（可选）要使用的提示模板（覆盖 Modelfile 中定义的内容），内容为 Modelfile 文件格式的字符串
|
|
|
Template = """
|
|
|
<system>
|
|
|
You are a helpful assistant.
|
|
|
</system>
|
|
|
<user>
|
|
|
{prompt}
|
|
|
</user>
|
|
|
<assistant>
|
|
|
""",
|
|
|
|
|
|
//（可选）如果为 true，则不会对提示应用任何格式。如果您在对 API 的请求中指定了完整的模板化提示，则可以选择使用 raw 参数
|
|
|
// 在某些情况下，您可能希望绕过模板系统并提供完整的提示。在这种情况下，您可以使用raw参数禁用格式化。
|
|
|
//Raw = false,
|
|
|
|
|
|
//（可选）控制模型在请求后加载到内存中的时间（默认值：5M）
|
|
|
KeepAlive = "5m",
|
|
|
|
|
|
//(可选且已弃用）：从上一个请求返回的 context 参数 /generate，这可用于保持较短的对话记忆
|
|
|
//Context = new long[] { 25897,46546546,546458},
|
|
|
};
|
|
|
|
|
|
//处理响应
|
|
|
IAsyncEnumerable<GenerateResponseStream?>? responses = _ollamaApiClient.GenerateAsync(generateRequest);
|
|
|
|
|
|
StringBuilder stringBuilder = new StringBuilder();
|
|
|
|
|
|
await foreach (GenerateResponseStream? stream in responses)
|
|
|
{
|
|
|
stringBuilder.Append(stream?.Response);
|
|
|
}
|
|
|
_output.WriteLine(stringBuilder.ToString());
|
|
|
|
|
|
Assert.NotNull(responses);
|
|
|
Assert.NotEmpty(responses);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 加载模型
|
|
|
/// </summary>
|
|
|
[Fact]
|
|
|
public async Task Completion_Request_LoadModel_Test()
|
|
|
{
|
|
|
var generateRequest = new GenerateRequest()
|
|
|
{
|
|
|
//默认模型
|
|
|
//Model = ModelSelecter.ModelWithTool
|
|
|
};
|
|
|
|
|
|
//处理响应
|
|
|
GenerateDoneResponseStream? responseStream = await _ollamaApiClient.GenerateAsync(generateRequest).StreamToEndAsync();
|
|
|
|
|
|
Assert.NotNull(responseStream);
|
|
|
Assert.NotNull(responseStream.Response);
|
|
|
Assert.True(responseStream.Done);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 卸载模型
|
|
|
/// </summary>
|
|
|
[Fact]
|
|
|
public async Task Completion_Request_UnLoadModel_Test()
|
|
|
{
|
|
|
var generateRequest = new GenerateRequest()
|
|
|
{
|
|
|
//默认模型
|
|
|
//Model = ModelSelecter.ModelWithTool,
|
|
|
|
|
|
//保持活跃0时长，即立即卸载
|
|
|
KeepAlive = "0",
|
|
|
};
|
|
|
|
|
|
//处理响应
|
|
|
GenerateDoneResponseStream? responseStream = await _ollamaApiClient.GenerateAsync(generateRequest).StreamToEndAsync();
|
|
|
|
|
|
Assert.NotNull(responseStream);
|
|
|
Assert.NotNull(responseStream.Response);
|
|
|
Assert.True(responseStream.Done);
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 生成对话补全(OllamaApiClient)
|
|
|
|
|
|
/// <summary>
|
|
|
/// 流式对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ClientRequest_Streaming_Test()
|
|
|
{
|
|
|
var chatRequest = new ChatRequest()
|
|
|
{
|
|
|
Messages = new[]
|
|
|
{
|
|
|
new Message()
|
|
|
{
|
|
|
Role = ChatRole.User,
|
|
|
Content = "天空为什么是蓝的？",
|
|
|
}
|
|
|
},
|
|
|
|
|
|
Think = true,
|
|
|
};
|
|
|
|
|
|
StringBuilder responseText = new StringBuilder();
|
|
|
|
|
|
var chatResponses = _ollamaApiClient.ChatAsync(chatRequest);
|
|
|
|
|
|
await foreach (var response in chatResponses)
|
|
|
{
|
|
|
responseText.Append(response?.Message.Content);
|
|
|
}
|
|
|
|
|
|
_output.WriteLine(responseText.ToString());
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 非流式对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ClientRequest_NoStreaming_Test()
|
|
|
{
|
|
|
var chatRequest = new ChatRequest()
|
|
|
{
|
|
|
Messages = new[]
|
|
|
{
|
|
|
new Message()
|
|
|
{
|
|
|
Role = ChatRole.User,
|
|
|
Content = "天空为什么是蓝的？",
|
|
|
}
|
|
|
},
|
|
|
|
|
|
Think = false,
|
|
|
|
|
|
//（可选）禁用流式处理响应
|
|
|
Stream = false,
|
|
|
};
|
|
|
|
|
|
var chatDoneResponse = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
|
|
|
_output.WriteLine(chatDoneResponse?.Message.Content);
|
|
|
|
|
|
Assert.NotNull(chatDoneResponse);
|
|
|
Assert.NotNull(chatDoneResponse.Message?.Content);
|
|
|
Assert.True(chatDoneResponse.Done);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 结构化输出对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ClientRequest_StructuredOutputs_Test()
|
|
|
{
|
|
|
var chatRequest = new ChatRequest()
|
|
|
{
|
|
|
Messages = new[]
|
|
|
{
|
|
|
new Message()
|
|
|
{
|
|
|
Role = ChatRole.User,
|
|
|
Content = "Ollama is 22 years old and busy saving the world. Return a JSON object with the age and availability.",
|
|
|
}
|
|
|
},
|
|
|
|
|
|
Think = false,
|
|
|
|
|
|
//（可选）禁用流式处理响应
|
|
|
Stream = false,
|
|
|
|
|
|
Format = new
|
|
|
{
|
|
|
type = "object",
|
|
|
properties = new { age = new { type = "integer" }, available = new { type = "boolean" } },
|
|
|
required = new string[] { "age", "available" }
|
|
|
},
|
|
|
};
|
|
|
var chatDoneResponse = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
_output.WriteLine(chatDoneResponse?.Message.Content);
|
|
|
|
|
|
var jsonObject = new { age = 0, available = false };
|
|
|
|
|
|
var responseObject = Newtonsoft.Json.JsonConvert.DeserializeAnonymousType(chatDoneResponse?.Message.Content ?? "{}", jsonObject);
|
|
|
|
|
|
Assert.NotNull(responseObject);
|
|
|
Assert.Equal(22, responseObject.age);
|
|
|
Assert.True(responseObject.available);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 带历史上下文的对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ClientRequest_WithHistory_Test()
|
|
|
{
|
|
|
var chatRequest = new ChatRequest()
|
|
|
{
|
|
|
Messages = new List<Message>(),
|
|
|
|
|
|
Think = false,
|
|
|
|
|
|
Stream = false,
|
|
|
};
|
|
|
|
|
|
var history = new List<string>();
|
|
|
List<Message> messages = new List<Message>()
|
|
|
{
|
|
|
new Message { Role="user", Content = "为什么天空是蓝色的？"},
|
|
|
new Message { Role="assistant", Content = "due to rayleigh scattering."},
|
|
|
new Message { Role="user", Content = "how is that different than mie scattering?使用中文回答"}
|
|
|
};
|
|
|
|
|
|
foreach (var message in messages)
|
|
|
{
|
|
|
List<Message> current = chatRequest.Messages.ToList();
|
|
|
current.Add(message);
|
|
|
|
|
|
chatRequest.Messages = current;
|
|
|
|
|
|
var chatDoneResponse = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
history.Add(chatDoneResponse?.Message.Content ?? "");
|
|
|
}
|
|
|
|
|
|
_output.WriteLine(string.Join(Environment.NewLine + "++++++++++++++++++++++++++++++++++++++" + Environment.NewLine, history));
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 带图像的对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ClientRequest_WithImages_Test()
|
|
|
{
|
|
|
var chatRequest = new ChatRequest()
|
|
|
{
|
|
|
Model = ModelSelecter.ModelWithVision,
|
|
|
|
|
|
Messages = new[]
|
|
|
{
|
|
|
new Message()
|
|
|
{
|
|
|
Role = ChatRole.User,
|
|
|
Content = "这张图片的内容是什么？用中文回答！",
|
|
|
Images = new string[]
|
|
|
{ "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"
|
|
|
},
|
|
|
}
|
|
|
},
|
|
|
|
|
|
Think = false,
|
|
|
|
|
|
Stream = false,
|
|
|
};
|
|
|
var chatDoneResponse = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
_output.WriteLine(chatDoneResponse?.Message.Content);
|
|
|
|
|
|
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 可复现输出的对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ClientRequest_ReproducibleOutputs_Test()
|
|
|
{
|
|
|
var chatRequest = new ChatRequest()
|
|
|
{
|
|
|
Messages = new[]
|
|
|
{
|
|
|
new Message()
|
|
|
{
|
|
|
Role = ChatRole.User,
|
|
|
Content = "为什么天空是蓝的？",
|
|
|
}
|
|
|
},
|
|
|
|
|
|
Think = false,
|
|
|
|
|
|
Stream = false,
|
|
|
|
|
|
Options = new RequestOptions()
|
|
|
{
|
|
|
Seed = 19491001,
|
|
|
Temperature = 0f,
|
|
|
|
|
|
},
|
|
|
};
|
|
|
|
|
|
|
|
|
//第一次请求
|
|
|
var doneResponse = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
var responseText1 = doneResponse?.Message.Content;
|
|
|
|
|
|
//第二次请求
|
|
|
var doneResponse2 = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
var responseText2 = doneResponse?.Message.Content;
|
|
|
|
|
|
Assert.Equal(responseText1, responseText2);
|
|
|
|
|
|
_output.WriteLine(responseText1);
|
|
|
_output.WriteLine(responseText2);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 支持工具调用的对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <remarks>
|
|
|
/// 携带工具本地执行结果，再次发送给大模型不
|
|
|
/// </remarks>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ClientRequest_WithTools_Test()
|
|
|
{
|
|
|
List<Message> Messages = new List<Message>();
|
|
|
Messages.Add(new Message() { Role = ChatRole.User, Content = "获取北京市当前的时间？", });
|
|
|
|
|
|
var chatRequest = new ChatRequest()
|
|
|
{
|
|
|
Model = ModelSelecter.ModelWithRawmodel,
|
|
|
|
|
|
Messages = Messages,
|
|
|
|
|
|
Stream = false,
|
|
|
|
|
|
Tools = new[]
|
|
|
{
|
|
|
new
|
|
|
{
|
|
|
type = "function",
|
|
|
function = new
|
|
|
{
|
|
|
name = "GetCurrentTime",
|
|
|
description = "获取指定城市的当前时间",
|
|
|
parameters = new
|
|
|
{
|
|
|
type = "object",
|
|
|
properties = new
|
|
|
{
|
|
|
city = new
|
|
|
{
|
|
|
type = "string",
|
|
|
description = "城市名称"
|
|
|
}
|
|
|
}
|
|
|
},
|
|
|
required = new string[] { "city" }
|
|
|
},
|
|
|
}
|
|
|
},
|
|
|
};
|
|
|
|
|
|
//第1步：向大模型发送带tool信息的请求
|
|
|
var doneResponse = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
|
|
|
//第2步：接收大模型响应，解析响应中携带有“工具调用信息”
|
|
|
|
|
|
//响应信息
|
|
|
Message toolResponseMessage = doneResponse!.Message;
|
|
|
|
|
|
//加入请求信息列表
|
|
|
Messages.Add(doneResponse.Message);
|
|
|
|
|
|
//函数信息
|
|
|
var currentFunction = toolResponseMessage.ToolCalls!.First().Function;
|
|
|
|
|
|
//参数信息
|
|
|
var city = currentFunction?.Arguments!["city"]?.ToString() ?? string.Empty;
|
|
|
|
|
|
//第3步：使用大模型响应中的函数信息，调用本地工具函数，获取执行结果
|
|
|
|
|
|
/*真实项目应该用特性、反射等技术，动态执行。此处仅作演示，直接写死第一个函数*/
|
|
|
|
|
|
//调用本地工具函数
|
|
|
var callCurrentDate = GetCurrentTime(city);
|
|
|
|
|
|
//第4步：传递工具执行结果给大模型，大模型生成最终回复
|
|
|
|
|
|
//函数调用信息
|
|
|
var ss = string.Join(", ", currentFunction?.Arguments?.Select(kvp => $"{kvp.Key}:{kvp.Value}") ?? new string[] { });
|
|
|
|
|
|
var toolResultText = $"{currentFunction?.Name ?? "(unnamed tool)"}({string.Join(", ",ss)})";
|
|
|
|
|
|
Messages.Add(new Message() { Role = ChatRole.Tool, Content = $"Tool:{toolResultText}:\nResult:{callCurrentDate.ToString("yyyy-MM-dd HH:mm:ss")}"});
|
|
|
chatRequest.Messages = Messages;
|
|
|
|
|
|
//理论上：大模型应该根据工具调用结果，生成最终回复，但此处没有真正起到作用
|
|
|
|
|
|
var doneResponse2 = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
var lastContentText = doneResponse2?.Message.Content;
|
|
|
|
|
|
_output.WriteLine(lastContentText);
|
|
|
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 生成对话补全(Chat方便、功能不全,应该在开发中)
|
|
|
|
|
|
/// <summary>
|
|
|
/// 流式对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ChatRequest_Streaming_Test()
|
|
|
{
|
|
|
var myChat = new Chat(_ollamaApiClient)
|
|
|
{
|
|
|
Think = true,
|
|
|
};
|
|
|
|
|
|
StringBuilder responseText = new StringBuilder();
|
|
|
|
|
|
IAsyncEnumerable<string>? chatResponses = myChat.SendAsync("天空为什么是蓝的?");
|
|
|
|
|
|
await foreach (var response in chatResponses)
|
|
|
{
|
|
|
responseText.Append(response);
|
|
|
}
|
|
|
|
|
|
_output.WriteLine(responseText.ToString());
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 非流式对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ChatRequest_NoStreaming_Test()
|
|
|
{
|
|
|
var myChat = new Chat(_ollamaApiClient)
|
|
|
{
|
|
|
Think = true,
|
|
|
};
|
|
|
|
|
|
var chatDoneResponse = await myChat.SendAsync("天空为什么是蓝的？").StreamToEndAsync();
|
|
|
|
|
|
_output.WriteLine(chatDoneResponse);
|
|
|
|
|
|
Assert.NotNull(chatDoneResponse);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 结构化输出对话请求(没有直接实现)
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public void ChatRequest_StructuredOutputs_Test()
|
|
|
{
|
|
|
//var chatRequest = new ChatRequest()
|
|
|
//{
|
|
|
// Messages = new[]
|
|
|
// {
|
|
|
// new Message()
|
|
|
// {
|
|
|
// Role = ChatRole.User,
|
|
|
// Content = "Ollama is 22 years old and busy saving the world. Return a JSON object with the age and availability.",
|
|
|
// }
|
|
|
// },
|
|
|
|
|
|
// Think = false,
|
|
|
|
|
|
// //（可选）禁用流式处理响应
|
|
|
// Stream = false,
|
|
|
|
|
|
// Format = new
|
|
|
// {
|
|
|
// type = "object",
|
|
|
// properties = new { age = new { type = "integer" }, available = new { type = "boolean" } },
|
|
|
// required = new string[] { "age", "available" }
|
|
|
// },
|
|
|
//};
|
|
|
//var chatDoneResponse = await _ollamaApiClient.ChatAsync(chatRequest).StreamToEndAsync();
|
|
|
//_output.WriteLine(chatDoneResponse?.Message.Content);
|
|
|
|
|
|
//var jsonObject = new { age = 0, available = false };
|
|
|
|
|
|
//var responseObject = Newtonsoft.Json.JsonConvert.DeserializeAnonymousType(chatDoneResponse?.Message.Content ?? "{}", jsonObject);
|
|
|
|
|
|
//Assert.NotNull(responseObject);
|
|
|
//Assert.Equal(22, responseObject.age);
|
|
|
//Assert.True(responseObject.available);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 带历史上下文的对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ChatRequest_WithHistory_Test()
|
|
|
{
|
|
|
var myChat = new Chat(_ollamaApiClient);
|
|
|
|
|
|
List<Message> messages = new List<Message>()
|
|
|
{
|
|
|
new Message { Role="user", Content = "为什么天空是蓝色的？"},
|
|
|
new Message { Role="assistant", Content = "due to rayleigh scattering."},
|
|
|
new Message { Role="user", Content = "how is that different than mie scattering?使用中文回答"}
|
|
|
};
|
|
|
|
|
|
foreach (var message in messages)
|
|
|
{
|
|
|
var chatDoneResponse = await myChat.SendAsAsync(message.Role!.Value, message.Content??"").StreamToEndAsync();
|
|
|
_output.WriteLine(chatDoneResponse);
|
|
|
_output.WriteLine("----------------------------------------------------");
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 带图像的对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ChatRequest_WithImages_Test()
|
|
|
{
|
|
|
var myChat = new Chat(_ollamaApiClient)
|
|
|
{
|
|
|
Model = ModelSelecter.ModelWithVision,
|
|
|
};
|
|
|
|
|
|
var base64img1 ="iVBORw0KGgoAAAANSUhEUgAAAFcAAABWCAYAAAC6lArJAAAACXBIWXMAABnWAAAZ1gEY0crtAAAAEXRFWHRTb2Z0d2FyZQBTbmlwYXN0ZV0Xzt0AAAAXdEVYdFVzZXIgQ29tbWVudABTY3JlZW5zaG9093UNRwAAFi9JREFUeJztnXt0VEWexz91b7/SnRdBIBCIgCKIQYMZdVRcFQQGH8dBYVdR1jPrzAioOO7q4OiemdHVgw7r4zDjjiP4GB8wGgdGGJQoCshLMEp4CfIIAcIrpEnS6fTz3qr9o5NApztJd+iOzuN7zk2n6/7u/VV9b/WvflX1q7qCNnhr9c4RUokfA+OAAYDr9PNKqbaXdAghRNLy7elI9l7dgCYFhzTUR6Dm33n1+dtOP9ma27kf7LHnOsLPo2nTTk9vi3+S2y4UQrzU0ysevP76IUFoJnHuB3vsORnGh8C17RVAAE6HlZ6ZdnJddjIdVlwOKw6rjtWiYdE0NE2gCYEQIIh8fpegFCgUSoFUCikVhlSEDZNA2MQXNGgMhKlvCuJuDOAPGiRXlUDTtJU9Gplw/fVDghaAXEf4eYW4Np6wzaIxuE8O5+TnkOuyoX3XGEsCLQ8dAXrkD3YAuyVGVipFnTdI5XEPlTUewoZMVM21J13yOeBe8drqnSM0yRaaa3FLzdWE4Nz8HC4c2JMMW6zyfyT4ggYVVbXsr2ns1Cw286dQ5kUWXfFj1cbGOu0WrhiaT98ervh3+AdDCx9n98piw+5jBEJmZ5cIoel3a0ox7vTUvEw7Pygu/CexcVCQ52L8RYXkuuztyrTUbAXjNSHEACEEQgh6ZmUw5sIBuBzW7srv3xyyMqyMGVFArstOC29tDwAUAzSa/Vin3cI1F/TDYdW/xaz/bSDD1syVrUOuXBaINF5XDM3/ztTYUCiEz+fD5/MRDAZRSmGxWHA6nTidThwOB5qmfat5zHRYuXxIH1Z9fYT22jgLwLn5Od+qjQ2Hw+zdu5eVK1fyySefsG3bNmprawkGgxiGgVIKTdOw2WxkZWUxePBgrrzySsaPH09JSQnZ2dnfSr775bkY3Cebfcc8cc+Ld9buUTddMvBbcbc8Hg+LFy9m7ty5bNu2jXA4nNT1Qgh69+7NlClTmDZtGkOGDOn2XpwvaLD0ywMYZqwfLL7Ye1x975ze3ZohwzBYvHgxs2bNoqqqKukudTw4nU5+8pOf8Nhjj9GrV68U5DJxlO87wTdH6mPShbsxoPIy23ctUo0TJ04wY8YMFi1ahJQJ93oSRmFhIa+++ipjxoxJ+b3bw0lvkA83H4xJ13q4bN2WiV27djFq1Cjee++9tBALcPDgQW644QZ+97vfpU1HW+S6bDjjdKG17rJRX3/9Nddddx27d+9Ou65gMMjPfvYzZs+enRKT0xk0ITgryxGbnnbNQHV1NTfeeCOHDx/uDnUAmKbJr371K+bPn98t+nKcsRYg7eQGAgGmTp3K/v37060qBqZp8sADD7Bhw4a068rMiO0jpNX/Ukrx3HPPsWrVqg7l7HY7kyZNori4mC1btlBaWkowGGxXvri4mIkTJxIIBFiwYAEHDhxoV9bv9/OjH/2I8vJyMjMzu1qUTuGyxemAqTRi165dyuVyKaDdw263q0WLFkVd99e//lXZ7fa48pMnT1Z+v79VtqamRhUXF3eoA1APP/xwOouq6puC6q3PdkcdaSPXNE01adKkTgs9bdo0JaWMuf7RRx+Nke3Xr59yu90xsuXl5cpms3Wox+l0qsrKynQVV3kD4Rhy02Zzt2/fzpIlSzqUsVqtzJw5M26vavr06dhs0Y3EXXfdRV5eXoxscXExJSUlHery+Xw89dRTCeS8a7BosWVIC7lKKZ5//nlCoVCHcr1792bw4MGt39evX09xcTGPPvooBQUFDB8+/FRGNY1x48a13t/tdlNXV4dSCl3XE+o0vPPOOxw7dqyLpeoYWneR63a7Wbx4cadyubm5WCyRNlVKycyZM9myZQvPPPMMmzdvZtCgQacyqmkUFhayefNmRo0aRf/+/SkoKGDMmDE0NDRQUFDQqT6v18vrr7/e5XJ1hHhzi2khd8mSJTQ0NHQqFwqFWntRpmly5MgRIEJ0dXV1TA/L6/UyZcoUbr75Zr788kv+8pe/cPHFF2O1WvH5fAnlbeHChWnpucXti6XasEsp1Q033NBpQwaonJwcVVdX13rt9OnTFaB69eqljh07poqKilplNU1TK1asUAcOHIir94477khIp67raufOnakutpJSpb9B83q9rFu3LiFZj8fD8uXLW7/PmTOHN954g1WrVuH1evnmm29az0kpWbZsGYWFhTH3qaurY8WKFQnpNE2TDz/8MCHZZBCv5qac3C1btlBfHzv8Fg9KKWbPnk1TUxMALpeLqVOncv755zNnzpyY8d0333yT6urqmHu89NJLHD9+POE8dtapSRVSTm6yXc2tW7cydepUampqkFLi9/t54YUX4o4J1NbWMnnyZKqqqpBSEggEeO2113j88ceT0lleXo5hGEld0xUIpVI7bHTnnXfy9ttvJ31dz549GTZsGMeOHaOysrLD0azs7GyKiopwu93s2bMn6QZK13UqKyvjmpgzwdtr9kR9T/nYws6dO7t0ndvtZsOGddgskJUBNovAZoGWeUilIGxAyIBQ0MOmjesxTZKO5YKI3d27d2/KyW2LlJIbDodjbGK7inXo20MwvFAwcrDGiLMFg/MFvbIFmQ6wWQW6iG4oTAmGCb6gor4JDp5Q7Dgk2bJfsbVKUlWjaAokltd9+/YxevToLpQycaSUXK/X26l/m+sS3HmNxl2jdYYWaNi7MJuf7RTk94Bh/QXjRkaqtinheL1i6SbJ75cb7DzUcZ1u8anTiZSSW19f3+EMrkWH9x6xctXw1PdddA365Qnu+YHOHdfoXPZQkL1H2ye4trY25Xloi5SW0ufzddi4GCY89qbB5krZJVuZCGo9iv95x2D/8Y41eDzxYw1SiZTW3I4GuFuwcbfkX34R4ubv6zxwk87IQRqWFERQHXYr3lxlMnepibux80eXSF7PFCklN1GvLmRA6VqTRetNRpwdsb/jR2oM7CPQk/gtuRsV63dJFqyWLP/KxJd+vpJCSsm1WpNrnUwJFfslFa9IrDoMyhdcW6TxvSEa5/YV9M4RuBygCQiG4aRXcfCEYtsBxcptkq/2SXyBrrljup7+gMOUkpsXNvi+zcGOcIhGlZxjHzZh92HF7sMmfygzESJCaiTUHqSieS3DmeXRgqDQYuFSPf1BhyklN3fJByzpNYBGKfkqHGBt0M9XoQDfGCHcpkGn8dinQSkwz5BIAbg0jbN1KxdZ7Vxqz2CULYP+Fiv6V1+jPI2I7KwzU3K6vjYrkVJGrmpqIrh4KQBZmsbVdidX250AGEpxVBpsD4fYEQ6yzwhRZYQ5ZhrUSUlASbo6wioAqxBkCY3eus4A3co5FhvnWW2MsNo5x2LFJWINuaqvJ7hsOY7bJ3dRc+dIGbnB5SuQJ+viKxGCAbqVAbqVCY5IqKoATMCvJG7T5IQ0qZEmJ00TjzJpUoqAUhhKIYkQaAWcQiNLE/TQdM7SLPTWdc7SdLKEhk2IpOyvf8G7OP7t1lN97BQjNeQqReCNBcldQsTJdgkNl0WjkDO3gclaEWPrdoyvd2EpGt65cII4fbI1JY/M2PkN4a8qUnGr7oVpElj4btpunxJyA38qpd3Y9e84gks/RKWpt3bG5KoGD8E/v5+KvHwrkCfrCC5PbIooWZwxucvLynj8YCWfh/x4VfrGDFKNsFLsM8K81tTAw//3YnpmhM9kJkIpxYQJEygrK0MAuZpOkdXOKHsGl9syOM9qI0fT+bYXXynApySHDIPysJ91QT/loQDVhoGJwm63U1FRwbBhw85Iz9tr9rQ2aEqpM/MWqqqqWLlyZWsB6qTJmqCPNUFfZJW70BjZJ58PnnsBsXMX4U1fYmz/+owK0CE0DWG1IDIz0fr1xTJ0CJYLi5i39jOe+OPr1Ekzrj8dDAZZsGABTzzxREqzc0bkLlq0qN2QJQU0KYmvXz6Zt01CCEFo9Voa7viPTu+r5feJ/GOa0Q1lC3kZTkRWJqJHLlqf3ugF/dAH9EcbWIien4/okYPIyGidxnDKEO7XXulQ5zvvvMNjjz2G3Z669SFdJtc0Td54441O5fr165fc8iUhyHlrPvqgQSDbTJJpGkLXI06/JtoJc4lF//79O5WprKykoqKCyy67LPG8doIuN2g7d+5kx44dncrl5+cnf3NNR9htiIwMhPO0w2EHqyUy7ZDEA+vVq1enD9gwDBYsSK4j1Bm6TO7y5csxzc6HYuKFfHY3srISG5xZtmxZSgfRu0SulJL33nsvIdlEC5ZOOByOhEzTwYMH2bVrV8r0doncI0eOUFGRWHc32QH0dEDX9YTIDYfDLFu2LGV6u0Tupk2bEv75pDigp0uI2gehE5SVlXU5z0JTICQIidBU18ht8W0TQXfEZHUG0zQT7oHt2LEjodjiRJA0uVJKVq9enbC81+tNVkXK0bJnQyKor69n7969KdGbNLknT55MSnld3akBdJGdhX52ISIrE1omCJU6dQDoOiLO+oIzQXsPOJ6pME2TjRs3pkRv0p2IQ4cOxbW35513Hi6Xi82bN0elnx42ZB15EXmflaGCQVSDB3myDuXzRaaBNYFwOdHyeqL1Se2S/pqampiaK4Rg8uTJvPtu7HhueXl5SvQmTe7+/ftj7JfFYuHFF1/kxRdfjCH3wIEDSClPbZui6winE+F0ovXtQgejC4i3NFbTNH75y1+yf/9+vvjii6hz+/btQyl1xhtjJG0Wjh49GpM2bdo0rrvuOoYOHRpzrrq6msbGxq7lLkXYtm1bTFpmZiYDBw7k5ZdfxuWK3oLm+PHjCXWQ2kJTbY5kb9A2JH/EiBE8/fTTAFx66aUx8o2NjezZsycmvbsgpWTNmjUx6RdccAEul4vi4mKefPLJqFrq8/mS3hImXnvZJW+hBbm5uSxcuLD1yV911VVkZGREyRuGwccff5ysmpTh8OHDcQOyx48f3/r/fffdx8SJE1u/d8XPVXGmCcTJhiZlhA2UEm2EZfNn86XN1wYCQZqaIq2v05WJzWZDoRBK4LBbmXHP3ZSWRjcSJSUlbNy4sVtCiNri1Vdf5e67745K03WdrVu3Rq3QrKurY/To0VRUVDB06FC2b9/eugAxEZhSUbo++hcqauu8KuO0CGR12t82H6dkVGyqUgqPN4C7toaRF54f9fRtNhsbN26kuLg44cymAqZpMmrUKD7//POo9NGjR7NixYqYBuvw4cPcdtttDBs2jHnz5iWlyzAl722IdlEtrgwbjq6Ed8eBUmDKsygpKYlyZ0KhEHPmzOGtt97q1q2p1q1bx6ZNm6LSNE3jkUceiZuPgoICVq1a1aXGzIwTxCb8gZBqj1yp4NNqP3WBaNerpeIqpejnsjCqvwMBNDT6qW/0s3vHl0yYMCEqkxkZGa0Lp7sD4XCYMWPGxDRmN910E++//37KH7IvaLB0U2VUWodG5f1KH/evdkelKQVKKkxToUyFUopFN+dz1YCM5l2VFWPGjGHq1KlRi5j9fj/3338/K1asSOlUSnt4/fXXWbt2bVRaXl4ec+fOTcuvJ2zE1nato3Yx0ypObayrQJoK05AYYYlpSExTYRqKQx6jVUbJyNaAzz77bMxs6rp163jiiSfSviVVRUUFDz30UHTEocXCb3/7WwYOHJgWnYFwLLmWjiJlxhZm8Okt+dT6I2QYUvHUhjrKjwZQUiElSFOimu1NZIggIpuXl0dpaSmjR4/mxIkTzecVc+bMobCwkJ/+9KdpqUH79u3j1ltvjVrzIITgkUce4fbbb0+5vhb4g7Gjfx3WXIAhuVYu72vn+/l2lu5p4osjfqSpWmtxOCRpqYgKFRVTW1RUxNKlS6O2/wuHw8ycOZNnn3025cORW7ZsYdy4cVRWnrJ9mqYxc+ZMfv3rX6e1MW0MhFGCqENLJETGVDBrdS3zKxpaib003044aGKG5amfn4p1pi8rGc6Hb/2CQX1O+bihUIhZs2YxZcqUlOw1FgwGmTdvHldffXUUsTYL/Gr65fzvkw+ip3ikrS0a4izI6LTmSgWzVtYyf7MHKSPEThuZw6NX5GGGI7W31SxAsxOsUE1VmNv/m/BHFzHC+xirnrIwoUSjpYxSSkpLSykuLubpp5/m2LFjSfeM/H4/ZWVlXHPNNdxzzz1Rg9z98gR/etjGrNFfIj8ZibHhFlTNJyCT69YmAqkU7sbYpZvC6wsoV0b81ruF2HkVDSgZ6aXNKMll9rVn8VmVj9Hzq1BS8dqkAu4qyaW2zou7zsO5vhcxK/8ARlPU/QwTFn5m8vifDA7VRhOZk5PD2LFjueWWW7j44ovp06cPTqezdf5LSkkwGKS+vp7du3fz0UcfUVpaGjNK57DBv47SeepOC71z2tRWoSF6XILloucQPS7uIpWxcDcG+HhL7EaZwtsUUC5nLLmmgp9/WssrFQ1IGXGxZpTk8vSYXghgVWUTo+dVAUSRW3N0L+fuurzDzDT44OUyg1c/Ntlfo2LaVKvVisvlIicnh+zsbHRdx+fz0dDQgMfjIRAIxNTybCf8YKTOrFstXHC2aP8VLQB6BtYr/ozoFffVGEmjfF8Ne4/G7jFhifdDVApmfXqC+ZsbIo2VUtz7vVxmNxMLcJZTj8TeK+ibbWm+TiGNzlc25zjh4YkW7r/RwpodkoWfmazfJTnsVoSMSKNXX1/f4aYYmoAemYKiswU/vEzn1is0+uQmaFdNP2bVH7GkgNymYJiqmvjxvTGdCFPBzz85wbzNzaZAKmZc0iOKWICifAdL/r0QT0Ay9tzTtvHTsxA5I1CBoxGzIMOAbLXFp8NhFYwt1hk70kLYFBytE1RU6eyotnLwpJ26gAufzEUJOzZVT7bVQ59sP+flhxk50GBIX4nLrhBCneo2xkCA0EDooGcgbD0QWcPQz5mRLI8xUMDWKnfcXZ8BhMfr92a5IqtAlIL/WnGCeV/Vt/bE7r2kB8+M7dXxz6wZNe5Gjrs9jBiSDzKMMn1geMFoBDOAkmGiCNAsCM0Gmh10B2hOhGYF3QbCCkIjMkEtAIlQEpQJMoRSITADYPojnzKEUgacvv5N6AjdAboTrNkI3Qm6HYSFDt7tlDCq3V7W7TzSnsPVZAEOAcNMBQ+vOMHLX9a3Nl73XpLLM9clRizQ+lKhSC1pLpjtVDhTV4pz6ho9cl+soDvi3qs7dyv3+ENs2nO8I0/2kKaU+gjgzzsb+UN5XcSPlREb+5uxvZN7M9RpPbS/Z/iCBmu+PkIoznhCCxSiTNOFnA8op0Wgmom975Ie/GZcksTSvHHXdyDCJp3w+EOs3F5No7/DrRSVphnzLZmZmduUUi/dcF7m9AW39kMqmHh+VpfeZaaa3zH29wgFHHZ72bTneIc1FkDA72+7cvj2Fm/hQQHDfnh+1hn5JpFK+7f7vrT20BQIs+2Am4M1HmRnxRN8mu3R/hOaXTEhRFApNaHB43tOwXRAxNQ/deqf9nzjmpMenI7u270/nWh52dz+4w1UHffEnWloAyXg91pu+MHrRxWFIE41K9+6b4QU2t1SqvECBigVeTGSiozKtI4fxM6rKTKdGZw3qA92m/6df02iUgpTRo6wYRJsfk2it/k1iScbA/hDsaN2beZxm4BDClEmpflK2xd8/j8/QLZ5bjNFpwAAAABJRU5ErkJggg==";
|
|
|
|
|
|
var chatDoneResponse = await myChat.SendAsync("这张图片的内容是什么？用中文回答！", new string[] { base64img1 }).StreamToEndAsync();
|
|
|
|
|
|
_output.WriteLine(chatDoneResponse);
|
|
|
|
|
|
Assert.NotNull(chatDoneResponse);
|
|
|
Assert.Contains("企鹅", chatDoneResponse);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 可复现输出的对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ChatRequest_ReproducibleOutputs_Test()
|
|
|
{
|
|
|
//第一次请求
|
|
|
var myChat1 = new Chat(_ollamaApiClient)
|
|
|
{
|
|
|
Think = false,
|
|
|
Options = new RequestOptions()
|
|
|
{
|
|
|
Seed = 19491001,
|
|
|
Temperature = 0f,
|
|
|
},
|
|
|
};
|
|
|
var doneResponse1 = await myChat1.SendAsync("通常情况，天空是什么颜色？").StreamToEndAsync();
|
|
|
|
|
|
//第二次请求
|
|
|
var myChat2 = new Chat(_ollamaApiClient)
|
|
|
{
|
|
|
Think = false,
|
|
|
Options = new RequestOptions()
|
|
|
{
|
|
|
Seed = 19491001,
|
|
|
Temperature = 0f,
|
|
|
},
|
|
|
};
|
|
|
var doneResponse2 = await myChat2.SendAsync("通常情况，天空是什么颜色？").StreamToEndAsync();
|
|
|
|
|
|
Assert.Equal(doneResponse1, doneResponse2);
|
|
|
|
|
|
_output.WriteLine(doneResponse1);
|
|
|
_output.WriteLine(doneResponse2);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 支持工具调用的对话请求 测试
|
|
|
/// </summary>
|
|
|
/// <remarks>
|
|
|
/// 携带工具本地执行结果，再次发送给大模型不
|
|
|
/// </remarks>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task ChatRequest_WithTools_Test()
|
|
|
{
|
|
|
var chat = new Chat(_ollamaApiClient) {Model = ModelSelecter.ModelWithRawmodel};
|
|
|
|
|
|
List<object> Tools = [new GetWeatherTool(), new GetLatLonAsyncTool(), new GetPopulationTool()];
|
|
|
|
|
|
StringBuilder outputString = new StringBuilder(1000);
|
|
|
|
|
|
//各个步骤，被Chat类包装了
|
|
|
await foreach (var answerToken in chat.SendAsync("上海市的天气怎么样？", Tools))
|
|
|
{
|
|
|
outputString.Append(answerToken);
|
|
|
}
|
|
|
|
|
|
_output.WriteLine(outputString.ToString());
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 列出本地模型
|
|
|
[Fact]
|
|
|
public async Task List_Local_Models_Test()
|
|
|
{
|
|
|
var models = await _ollamaApiClient.ListLocalModelsAsync();
|
|
|
|
|
|
_output.WriteLine("本地模型有：");
|
|
|
foreach (OllamaSharp.Models.Model model in models)
|
|
|
{
|
|
|
_output.WriteLine(model.Name);
|
|
|
}
|
|
|
_output.WriteLine($"共：{models.Count()} 个");
|
|
|
|
|
|
|
|
|
Assert.NotNull(models);
|
|
|
Assert.True(models.Any());
|
|
|
}
|
|
|
|
|
|
[Theory]
|
|
|
[InlineData(ModelSelecter.ModelWithTool)]
|
|
|
[InlineData(ModelSelecter.ModelWithVision)]
|
|
|
[InlineData(ModelSelecter.ModelWithEmbedding)]
|
|
|
public async Task Exist_Local_Models_Test(string selectedModel)
|
|
|
{
|
|
|
var models = await _ollamaApiClient.ListLocalModelsAsync();
|
|
|
|
|
|
_output.WriteLine("本地模型有：");
|
|
|
foreach (OllamaSharp.Models.Model model in models)
|
|
|
{
|
|
|
_output.WriteLine(model.Name);
|
|
|
}
|
|
|
_output.WriteLine($"共：{models.Count()} 个");
|
|
|
|
|
|
Assert.NotNull(models);
|
|
|
Assert.Contains(models, m => m.Name == selectedModel);
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 列出运行中的模型
|
|
|
|
|
|
/// <summary>
|
|
|
/// 列出运行中的模型
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task List_Running_Models_Test()
|
|
|
{
|
|
|
var models = await _ollamaApiClient.ListRunningModelsAsync();
|
|
|
|
|
|
_output.WriteLine("运行中的模型有：");
|
|
|
foreach (OllamaSharp.Models.Model model in models)
|
|
|
{
|
|
|
_output.WriteLine(model.Name);
|
|
|
}
|
|
|
_output.WriteLine($"共：{models.Count()} 个");
|
|
|
|
|
|
|
|
|
Assert.NotNull(models);
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// 指定模型是否在运行中
|
|
|
/// </summary>
|
|
|
/// <param name="selectedModel"></param>
|
|
|
/// <returns></returns>
|
|
|
[Theory]
|
|
|
[InlineData(ModelSelecter.ModelWithTool)]
|
|
|
public async Task IsRunning_Models_Test(string selectedModel)
|
|
|
{
|
|
|
var models = await _ollamaApiClient.ListRunningModelsAsync();
|
|
|
|
|
|
_output.WriteLine("运行中的模型有：");
|
|
|
foreach (OllamaSharp.Models.Model model in models)
|
|
|
{
|
|
|
_output.WriteLine(model.Name);
|
|
|
}
|
|
|
_output.WriteLine($"共：{models.Count()} 个");
|
|
|
|
|
|
|
|
|
var isRunning = models.Any(m => m.Name == selectedModel);
|
|
|
var message = $"{selectedModel}模型：{(isRunning ? "运行中" : "未运行")}";
|
|
|
|
|
|
_output.WriteLine(message);
|
|
|
|
|
|
Assert.True(true, message);
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region Ollama服务运行状态
|
|
|
[Fact]
|
|
|
public async Task OllamaServer_IsRunning_Test()
|
|
|
{
|
|
|
var isRunning = await _ollamaApiClient.IsRunningAsync();
|
|
|
|
|
|
var ollamaState = $"Ollama服务：{(isRunning ? "运行中" : "未运行")}";
|
|
|
|
|
|
_output.WriteLine(ollamaState);
|
|
|
|
|
|
Assert.True(isRunning, ollamaState);
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region 版本信息
|
|
|
/// <summary>
|
|
|
/// 获取版本信息
|
|
|
/// </summary>
|
|
|
/// <returns></returns>
|
|
|
[Fact]
|
|
|
public async Task Get_Version_Test()
|
|
|
{
|
|
|
var version = await _ollamaApiClient.GetVersionAsync();
|
|
|
_output.WriteLine($"Version: {version}");
|
|
|
|
|
|
Assert.NotNull(version);
|
|
|
}
|
|
|
#endregion
|
|
|
|
|
|
#region tools 方法
|
|
|
/// <summary>
|
|
|
/// Gets the current weather for a given location.
|
|
|
/// </summary>
|
|
|
/// <param name="location">The location or city to get the weather for</param>
|
|
|
/// <param name="unit">The unit to measure the temperature in</param>
|
|
|
/// <returns>The weather for the given location</returns>
|
|
|
[OllamaTool]
|
|
|
public static string GetWeather(string location, Unit unit) => $"It's cold at only 6° {unit} in {location}.";
|
|
|
|
|
|
/// <summary>
|
|
|
/// Gets the latitude and longitude for a given location.
|
|
|
/// </summary>
|
|
|
/// <param name="location">The location to get the latitude and longitude for</param>
|
|
|
/// <returns>The weather for the given location</returns>
|
|
|
[OllamaTool]
|
|
|
public async static Task<string> GetLatLonAsync(string location)
|
|
|
{
|
|
|
await Task.Delay(200).ConfigureAwait(false);
|
|
|
return $"{new Random().Next(20, 50)}.4711, {new Random().Next(3, 15)}.0815";
|
|
|
}
|
|
|
|
|
|
/// <summary>
|
|
|
/// Gets the amount of people living in a given city
|
|
|
/// </summary>
|
|
|
/// <param name="city">The city to get the population info for</param>
|
|
|
/// <returns>The population of a given city</returns>
|
|
|
[OllamaTool]
|
|
|
public static int GetPopulation(string city) => new Random().Next(1000, 10000000);
|
|
|
|
|
|
public static DateTime GetCurrentTime(string city)
|
|
|
{
|
|
|
return DateTime.Now;
|
|
|
}
|
|
|
|
|
|
#endregion
|
|
|
}
|
|
|
|
|
|
public enum Unit
|
|
|
{
|
|
|
Celsius,
|
|
|
Fahrenheit
|
|
|
}
|
|
|
}
|