Initial commit: 健康管家 AI 健康陪伴助手
- Backend: .NET 10 Minimal API + EF Core + PostgreSQL - Frontend: Flutter + Riverpod + GoRouter + Dio - AI: DeepSeek LLM + Qwen VLM (OpenAI-compatible) - Auth: SMS + JWT (access/refresh tokens) - Features: AI chat, health tracking, medication management, diet analysis, exercise plans, doctor consultations, report analysis
This commit is contained in:
MingNian
152
backend/src/Health.Infrastructure/AI/AiClients.cs
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152
backend/src/Health.Infrastructure/AI/AiClients.cs
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@@ -0,0 +1,152 @@
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using Microsoft.Extensions.Configuration;
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using System.Net.Http.Headers;
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using System.Text;
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using System.Text.Json;
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namespace Health.Infrastructure.AI;
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/// <summary>
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/// DeepSeek LLM 客户端（对话 + Tool Calling）
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/// </summary>
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public sealed class DeepSeekClient
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{
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private readonly HttpClient _http;
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private readonly string _model;
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private readonly JsonSerializerOptions _jsonOptions;
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public DeepSeekClient(HttpClient http, IConfiguration config)
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{
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_http = http;
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_model = config["DEEPSEEK_MODEL"] ?? "deepseek-chat";
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_jsonOptions = new()
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{
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PropertyNamingPolicy = JsonNamingPolicy.SnakeCaseLower,
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PropertyNameCaseInsensitive = true
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};
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}
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/// <summary>
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/// 流式 Chat Completions
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/// </summary>
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public async IAsyncEnumerable<string> ChatStreamAsync(
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List<ChatMessage> messages,
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List<ToolDefinition>? tools = null,
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int maxTokens = 2048,
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float temperature = 0.7f,
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[System.Runtime.CompilerServices.EnumeratorCancellation] CancellationToken ct = default)
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{
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var request = new ChatCompletionRequest
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{
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Model = _model, Messages = messages, Stream = true,
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MaxTokens = maxTokens, Temperature = temperature, Tools = tools,
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};
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if (tools?.Count > 0) request.ToolChoice = "auto";
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var json = JsonSerializer.Serialize(request, _jsonOptions);
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var content = new StringContent(json, Encoding.UTF8, "application/json");
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var httpRequest = new HttpRequestMessage(HttpMethod.Post, "chat/completions") { Content = content };
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httpRequest.Headers.Accept.Add(new MediaTypeWithQualityHeaderValue("text/event-stream"));
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using var response = await _http.SendAsync(httpRequest, HttpCompletionOption.ResponseHeadersRead, ct);
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response.EnsureSuccessStatusCode();
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using var stream = await response.Content.ReadAsStreamAsync(ct);
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using var reader = new StreamReader(stream);
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string? line;
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while ((line = await reader.ReadLineAsync(ct)) != null)
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{
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if (string.IsNullOrWhiteSpace(line)) continue;
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if (!line.StartsWith("data: ")) continue;
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var data = line["data: ".Length..];
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if (data == "[DONE]") break;
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yield return data;
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}
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}
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/// <summary>
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/// 非流式 Chat Completions（用于 Tool Calling）
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/// </summary>
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public async Task<ChatCompletionResponse> ChatAsync(
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List<ChatMessage> messages,
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List<ToolDefinition>? tools = null,
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int maxTokens = 2048,
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float temperature = 0.7f,
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CancellationToken ct = default)
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{
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var request = new ChatCompletionRequest
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{
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Model = _model, Messages = messages, Stream = false,
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MaxTokens = maxTokens, Temperature = temperature, Tools = tools,
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};
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if (tools?.Count > 0) request.ToolChoice = "auto";
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var json = JsonSerializer.Serialize(request, _jsonOptions);
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var content = new StringContent(json, Encoding.UTF8, "application/json");
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var response = await _http.PostAsync("chat/completions", content, ct);
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response.EnsureSuccessStatusCode();
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var body = await response.Content.ReadAsStringAsync(ct);
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return JsonSerializer.Deserialize<ChatCompletionResponse>(body, _jsonOptions)!;
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}
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}
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/// <summary>
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/// 千问 VL 视觉客户端（食物识别 + 报告解读）
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/// </summary>
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public sealed class QwenVisionClient
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{
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private readonly HttpClient _http;
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private readonly string _model;
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private readonly JsonSerializerOptions _jsonOptions;
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public QwenVisionClient(HttpClient http, IConfiguration config)
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{
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_http = http;
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_model = config["QWEN_VISION_MODEL"] ?? "qwen-vl-max";
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_jsonOptions = new()
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{
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PropertyNamingPolicy = JsonNamingPolicy.SnakeCaseLower,
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PropertyNameCaseInsensitive = true
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};
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}
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public async Task<ChatCompletionResponse> VisionAsync(
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string systemPrompt,
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List<string> imageUrls,
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string? userText = null,
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int maxTokens = 2048,
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CancellationToken ct = default)
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{
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var messages = new List<ChatMessage>();
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if (!string.IsNullOrEmpty(systemPrompt))
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messages.Add(new ChatMessage { Role = "system", Content = systemPrompt });
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var contentParts = new List<object>();
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foreach (var url in imageUrls)
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contentParts.Add(new { type = "image_url", image_url = new { url } });
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if (!string.IsNullOrEmpty(userText))
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contentParts.Add(new { type = "text", text = userText });
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var userMessage = new ChatMessage
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{
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Role = "user",
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Content = JsonSerializer.Serialize(contentParts, _jsonOptions)
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};
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messages.Add(userMessage);
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var request = new ChatCompletionRequest
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{
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Model = _model, Messages = messages, MaxTokens = maxTokens, Stream = false,
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};
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var json = JsonSerializer.Serialize(request, _jsonOptions);
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var content = new StringContent(json, Encoding.UTF8, "application/json");
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var response = await _http.PostAsync("chat/completions", content, ct);
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response.EnsureSuccessStatusCode();
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var body = await response.Content.ReadAsStringAsync(ct);
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return JsonSerializer.Deserialize<ChatCompletionResponse>(body, _jsonOptions)!;
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}
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}
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235
backend/src/Health.Infrastructure/AI/OpenAiCompatibleClient.cs
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235
backend/src/Health.Infrastructure/AI/OpenAiCompatibleClient.cs
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using System.Net.Http.Headers;
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using System.Text;
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using System.Text.Json;
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namespace Health.Infrastructure.AI;
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/// <summary>
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/// OpenAI 兼容协议 HTTP 客户端，统一调用 DeepSeek / 千问 VL
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/// </summary>
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public sealed class OpenAiCompatibleClient
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{
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private readonly HttpClient _http;
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private readonly string _model;
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private readonly JsonSerializerOptions _jsonOptions;
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public OpenAiCompatibleClient(string baseUrl, string apiKey, string model)
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{
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_http = new HttpClient
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{
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BaseAddress = new Uri(baseUrl.TrimEnd('/') + "/"),
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Timeout = TimeSpan.FromSeconds(60)
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};
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_http.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey);
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_http.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
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_model = model;
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_jsonOptions = new JsonSerializerOptions
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{
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PropertyNamingPolicy = JsonNamingPolicy.SnakeCaseLower,
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PropertyNameCaseInsensitive = true
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};
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}
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/// <summary>
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/// 流式 Chat Completions（SSE）
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/// </summary>
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public async IAsyncEnumerable<string> ChatStreamAsync(
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List<ChatMessage> messages,
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List<ToolDefinition>? tools = null,
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int maxTokens = 2048,
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float temperature = 0.7f)
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{
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var request = new ChatCompletionRequest
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{
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Model = _model,
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Messages = messages,
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Stream = true,
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MaxTokens = maxTokens,
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Temperature = temperature,
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Tools = tools,
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};
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if (tools?.Count > 0)
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request.ToolChoice = "auto";
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var json = JsonSerializer.Serialize(request, _jsonOptions);
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var content = new StringContent(json, Encoding.UTF8, "application/json");
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var httpRequest = new HttpRequestMessage(HttpMethod.Post, "chat/completions")
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{
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Content = content
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};
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httpRequest.Headers.Accept.Add(new MediaTypeWithQualityHeaderValue("text/event-stream"));
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var response = await _http.SendAsync(httpRequest, HttpCompletionOption.ResponseHeadersRead);
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response.EnsureSuccessStatusCode();
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using var stream = await response.Content.ReadAsStreamAsync();
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using var reader = new StreamReader(stream);
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string? line;
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while ((line = await reader.ReadLineAsync()) != null)
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{
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if (string.IsNullOrWhiteSpace(line)) continue;
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if (!line.StartsWith("data: ")) continue;
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var data = line["data: ".Length..];
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if (data == "[DONE]") break;
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yield return data;
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}
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}
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/// <summary>
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/// 非流式 Chat Completions
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/// </summary>
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public async Task<ChatCompletionResponse> ChatAsync(
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List<ChatMessage> messages,
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List<ToolDefinition>? tools = null,
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int maxTokens = 2048,
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float temperature = 0.7f)
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{
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var request = new ChatCompletionRequest
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{
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Model = _model,
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Messages = messages,
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Stream = false,
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MaxTokens = maxTokens,
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Temperature = temperature,
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Tools = tools,
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};
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if (tools?.Count > 0)
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request.ToolChoice = "auto";
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var json = JsonSerializer.Serialize(request, _jsonOptions);
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var content = new StringContent(json, Encoding.UTF8, "application/json");
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var response = await _http.PostAsync("chat/completions", content);
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response.EnsureSuccessStatusCode();
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var body = await response.Content.ReadAsStringAsync();
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return JsonSerializer.Deserialize<ChatCompletionResponse>(body, _jsonOptions)!;
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}
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/// <summary>
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/// Vision 图片理解（非流式）
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/// </summary>
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public async Task<ChatCompletionResponse> VisionAsync(
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string systemPrompt,
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List<string> imageUrls,
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string? userText = null,
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int maxTokens = 2048)
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{
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var messages = new List<ChatMessage>();
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if (!string.IsNullOrEmpty(systemPrompt))
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messages.Add(new ChatMessage { Role = "system", Content = systemPrompt });
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// 构建多模态消息内容
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var contentParts = new List<object>();
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foreach (var url in imageUrls)
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contentParts.Add(new { type = "image_url", image_url = new { url } });
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if (!string.IsNullOrEmpty(userText))
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contentParts.Add(new { type = "text", text = userText });
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var userMessage = new ChatMessage
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{
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Role = "user",
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Content = JsonSerializer.Serialize(contentParts, _jsonOptions)
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};
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messages.Add(userMessage);
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var request = new ChatCompletionRequest
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{
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Model = _model,
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Messages = messages,
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MaxTokens = maxTokens,
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Stream = false,
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};
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var json = JsonSerializer.Serialize(request, _jsonOptions);
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var content = new StringContent(json, Encoding.UTF8, "application/json");
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var response = await _http.PostAsync("chat/completions", content);
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response.EnsureSuccessStatusCode();
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var body = await response.Content.ReadAsStringAsync();
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return JsonSerializer.Deserialize<ChatCompletionResponse>(body, _jsonOptions)!;
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}
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}
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#region 请求/响应模型
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public sealed class ChatCompletionRequest
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{
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public string Model { get; set; } = string.Empty;
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public List<ChatMessage> Messages { get; set; } = [];
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public bool Stream { get; set; }
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public int MaxTokens { get; set; } = 2048;
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public float Temperature { get; set; } = 0.7f;
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public List<ToolDefinition>? Tools { get; set; }
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public string? ToolChoice { get; set; }
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}
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public sealed class ChatMessage
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{
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public string Role { get; set; } = string.Empty;
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public string Content { get; set; } = string.Empty;
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public string? ToolCallId { get; set; }
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public List<ToolCall>? ToolCalls { get; set; }
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}
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public sealed class ToolDefinition
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{
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public string Type { get; set; } = "function";
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public ToolFunction Function { get; set; } = new();
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}
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public sealed class ToolFunction
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{
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public string Name { get; set; } = string.Empty;
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public string Description { get; set; } = string.Empty;
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public object Parameters { get; set; } = new();
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}
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public sealed class ToolCall
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{
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public string Id { get; set; } = string.Empty;
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public string Type { get; set; } = "function";
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public ToolCallFunction Function { get; set; } = new();
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}
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public sealed class ToolCallFunction
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{
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public string Name { get; set; } = string.Empty;
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public string Arguments { get; set; } = string.Empty;
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}
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public sealed class ChatCompletionResponse
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{
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public string Id { get; set; } = string.Empty;
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public List<Choice> Choices { get; set; } = [];
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}
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public sealed class Choice
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{
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public int Index { get; set; }
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public ResponseMessage? Message { get; set; }
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public ResponseDelta? Delta { get; set; }
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public string? FinishReason { get; set; }
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}
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public sealed class ResponseMessage
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{
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public string Role { get; set; } = string.Empty;
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public string? Content { get; set; }
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public List<ToolCall>? ToolCalls { get; set; }
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}
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public sealed class ResponseDelta
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{
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public string? Content { get; set; }
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public string? Role { get; set; }
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}
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#endregion
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116
backend/src/Health.Infrastructure/AI/PromptManager.cs
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116
backend/src/Health.Infrastructure/AI/PromptManager.cs
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@@ -0,0 +1,116 @@
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using Health.Domain.Enums;
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namespace Health.Infrastructure.AI;
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/// <summary>
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/// System Prompt 模板管理
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/// </summary>
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public sealed class PromptManager
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{
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/// <summary>
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/// 获取指定 Agent 的 System Prompt
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/// </summary>
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public string GetSystemPrompt(AgentType agentType) => agentType switch
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{
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AgentType.Default => DefaultPrompt,
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AgentType.Consultation => ConsultationPrompt,
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AgentType.Health => HealthDataPrompt,
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AgentType.Diet => DietPrompt,
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AgentType.Medication => MedicationPrompt,
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AgentType.Report => ReportPrompt,
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AgentType.Exercise => ExercisePrompt,
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_ => DefaultPrompt
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};
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private const string DefaultPrompt = """
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你是一个心脏术后康复患者的私人 AI 健康管家，名叫"阿福"。
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语气温暖、专业、像朋友一样关怀患者。
|
||||
|
||||
职责：
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1. 理解用户的健康需求，解析健康数据
|
||||
2. 主动查看患者近期数据，发现异常时提醒
|
||||
3. 回答健康知识问题
|
||||
4. 每次回复末尾，如有需要提醒的事项，简短温馨地提醒一句
|
||||
|
||||
规则：
|
||||
- 不要提供超出你能力范围的医疗建议
|
||||
- 遇到紧急症状（剧烈胸痛、呼吸困难）立即建议就医
|
||||
- 饮食/运动建议要结合患者档案中的疾病和限制
|
||||
""";
|
||||
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||||
private const string ConsultationPrompt = """
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||||
你是一个心血管内科医生助手，负责对心脏术后患者进行多轮问诊。
|
||||
|
||||
规则：
|
||||
1. 每次只问一个问题，不要一次问多个
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||||
2. 给出 2-3 个快捷选项让患者点击
|
||||
3. 问诊步骤：先问感受 → 持续时间 → 伴随症状 → 近期用药 → 给出初步分析
|
||||
4. 遇到以下情况建议立即就医：剧烈胸痛、呼吸困难、心悸
|
||||
5. 遇到以下情况建议转医生：血压持续>160/100、心率>120或<50
|
||||
6. 所有分析末尾标注"以上为AI分析，具体请咨询医生"
|
||||
7. 问诊结束给出结构化小结
|
||||
""";
|
||||
|
||||
private const string HealthDataPrompt = """
|
||||
你是一个健康数据录入助手。
|
||||
|
||||
规则：
|
||||
1. 解析用户消息中的指标和数值（血压/心率/血糖/血氧/体重）
|
||||
2. 指标明确+数值明确→直接录入
|
||||
3. 数值明确但指标模糊（如只说"120"）→追问是"收缩压还是血糖？"
|
||||
4. 时间模糊→取当前时间，在确认卡片中告知用户
|
||||
5. 数值超出正常范围→附带异常提醒
|
||||
6. 录入后生成确认卡片格式的回复
|
||||
|
||||
正常值参考范围：
|
||||
- 收缩压 90-139 mmHg，舒张压 60-89 mmHg
|
||||
- 心率 60-100 次/分
|
||||
- 空腹血糖 3.9-6.1 mmol/L
|
||||
- 血氧 95-100%
|
||||
""";
|
||||
|
||||
private const string DietPrompt = """
|
||||
你是一个营养分析专家，专门为心脏术后患者提供饮食指导。
|
||||
|
||||
规则：
|
||||
1. 收到VLM食物识别结果后，结合患者档案进行综合分析
|
||||
2. 总热量汇总
|
||||
3. 逐项判断"能不能吃"（基于疾病诊断/过敏/饮食限制/近期指标）
|
||||
4. 给出 1-5 星健康评分
|
||||
5. 单项警告 + 整体饮食建议
|
||||
6. 追问餐次归属（早餐/午餐/晚餐/加餐）
|
||||
""";
|
||||
|
||||
private const string MedicationPrompt = """
|
||||
你是一个用药管理专家。
|
||||
|
||||
规则：
|
||||
1. 解析用户口中的药品信息（药名/剂量/频次/时间）
|
||||
2. "早饭后"等模糊时间→追问具体几点
|
||||
3. 解析完成后展示确认卡片
|
||||
4. 处方拍照→提取药品信息→生成用药计划→让用户确认
|
||||
5. 回答用药相关疑问
|
||||
""";
|
||||
|
||||
private const string ReportPrompt = """
|
||||
你是一个医学报告解读专家。
|
||||
|
||||
规则：
|
||||
1. 收到报告图片后，提取所有指标及其数值
|
||||
2. 标注异常指标（偏高/偏低/正常）
|
||||
3. 给出初步分析
|
||||
4. 所有内容标注"AI预解读，待医生确认"
|
||||
5. 图像类报告（彩超/CT）注明"需医生人工审阅"
|
||||
""";
|
||||
|
||||
private const string ExercisePrompt = """
|
||||
你是一个运动康复教练，专门为心脏术后患者制定运动计划。
|
||||
|
||||
规则：
|
||||
1. 帮助用户设定每周运动计划（类型/时长/天数）
|
||||
2. 以周为单位，每天指定运动类型和时长
|
||||
3. 推荐适合心脏康复的运动：散步、慢跑、太极、游泳等
|
||||
4. 运动强度要循序渐进
|
||||
5. 避免剧烈运动
|
||||
""";
|
||||
}
|
||||
Reference in New Issue
Block a user