9：MemNet记忆层使用，实现大模型对话上下文记忆

本文介绍了一个带记忆功能的聊天系统MemNet，该系统通过三个解耦但协作的模块实现：1)MemNet_Embedder将文本转换为向量；2)MemNet负责记忆的存储与检索；3)LLM结合当前问题和检索到的记忆生成回答。系统工作流程包括：用户输入问题→MemNet检索相关记忆→将记忆拼接到提示词中→LLM生成回答→存储新记忆。该系统能有效保持对话上下文，如能记住用户名、年龄等信息，并在后续对话中准

自己的九又四分之三站台

136人浏览 · 2026-02-02 21:45:00

自己的九又四分之三站台 · 2026-02-02 21:45:00 发布

1. 先上效果

1.1. MemNet记忆层效果

You：我叫蔡名洋
Memory:

AI：你好，蔡名洋！很高兴认识你。有什么我可以帮你的吗？

You：我今年32岁了
Memory:

AI：好的，你今年32岁。如果你有具体的问题，比如关于健康、职业发展、理财、生活规划等方面，可以告诉我，我会根据你的年龄和需求提供相应的建议！

You：现在不能吃辣的，不能喝凉的
Memory:

AI：听起来你目前可能有肠胃不适、上火，或者正在恢复期（比如刚做完手术、有口腔溃疡、胃炎等）。不能吃辣、不能喝凉的，通常说明你的消化系统或身体处于比较敏感的状态。

以下是一些建议，供你参考：

### 饮食建议：
- **避免辛辣刺激食物**：如辣椒、花椒、姜蒜（尤其是生的）、酒精等。
- **避免生冷饮品和食物**：冰水、冰淇淋、冷饮、生鱼片等暂时不要碰。
- **选择温热、清淡、易消化的食物**：比如小米粥、白粥、蒸蛋、煮软的蔬菜、面条、山药、南瓜等。
- **少量多餐**：减轻肠胃负担。
- **多喝温水**：有助于保护胃黏膜，促进代谢。

### 可能的原因（仅供参考）：
- 胃炎、胃溃疡
- 肠胃感冒或感染后恢复期
- 月经期（部分女性会暂时对寒凉敏感）
- 手术后/生病后体质虚弱
- 中医所说的“脾胃虚寒”或“湿热内蕴”

如果你的症状持续时间较长，或者伴有腹痛、腹泻、恶心、发热等情况，建议及时就医，明确原因。

需要我帮你搭配几天的温和饮食菜单吗？

You：我是男生
Memory:
用户今年32岁
用户的名字是蔡名洋。
AI：好的，蔡名洋！你是32岁的男生。有什么我可以帮你的吗？??

You：今年是2026年02月02日，请问我是哪一年出生的，我姓什么？
Memory:
2026-02-02 2:47:25用户是32岁的男生。
用户的名字是蔡名洋。
AI：根据你提供的信息：

- 今天是 **2026年2月2日**
- 你是 **32岁的男生**

那么你的出生年份是：
**2026 - 32 = 1994年**

但要注意，如果你的生日还没到（即在2月2日之后），那你实际上在2026年2月2日这天还只有31岁，要到生日那天才满32岁。不过你说“你是32岁的男生”，通常意味着**已经满32岁**，所以可以推断你的生日**在2月2日或之前**，因此出生年份为 **1994年**。

另外，你提到你的名字是 **蔡名洋**，所以你姓 **蔡**。

? 答案：
- 你出生于 **1994年**
- 你姓 **蔡**

You：

1.2. 无记忆层效果

You：我叫蔡名洋
AI：你好，蔡名洋！很高兴认识你。有什么我可以帮你的吗？

You：今年32岁
AI：好的，你今年32岁。请问你是想了解与年龄相关的健康建议、职业规划、保险配置，还是其他方面的信息呢？我可以根据你的需求提供更具体的帮助。

You：我叫什么
AI：我目前不知道你的名字。你可以告诉我你叫什么吗？??

2. 代码地址

3. 代码原理阅读

3.1. 代码原理

3.1.1.一句话版

MemNet_Embedder 负责文本 → 向量
MemNet 负责记忆的存储与检索（记忆层）
LLM 负责结合“当前问题 + 检索到的记忆”生成回答

这三者是解耦但协作的。

3.1.2. 更严格一点的职责划分

1️⃣ MemNet Embedder ——「向量化引擎」

配置项：

"Embedder": {
  "Endpoint": "...",
  "Model": "text-embedding-v4",
  "apiKey": "..."
}

职责只有一个：

把自然语言文本 → embedding 向量（float[]）

它什么时候被调用？

memoryService.AddAsync(...)
- 把 MessageContent 的文本转成向量并存储
memoryService.SearchAsync(...)
- 把 Query 转成向量，用来做相似度检索

它完全不参与“回答问题”

不看 prompt
不生成文本
只是数学意义上的语义表示

👉 这是一个纯 embedding service。

2️⃣ MemNet ——「记忆层（Memory Layer）」

你可以把 MemNet 理解成一个**“带语义索引的长期记忆系统”**：

它内部做的事通常包括：

能力	说明
记忆写入	存储文本 + embedding
向量索引	建 ANN / cosine / dot 索引
记忆检索	按语义相似度搜索
用户隔离	`UserId = "user001"`
生命周期	时间戳、更新、可能的清理策略

在你的代码里：

memoryService.SearchAsync(...)
memoryService.AddAsync(...)

MemNet 不负责回答问题，它只返回：

「和这个问题语义上最相近的历史内容」

👉 它是一个RAG 的“R（Retrieval）”组件。

3️⃣ LLM（qwen3-max）——「推理与生成引擎」

配置项：

"LLM": {
  "Endpoint": "...",
  "Model": "qwen3-max",
  "apiKey": "..."
}

在你的代码中是：

completeChatClient.GenerateTextAsync(question, memory)

它的职责是：

根据 prompt（system + user），生成自然语言回答

关键点在于：
LLM 并不知道什么是 MemNet，它只知道：

System: 这是和用户有关的记忆……
User: 当前问题

👉 在 LLM 看来，memory 只是 prompt 的一部分文本。

3.1.3. 三者的协作链路（你现在的代码）

用户输入 question
      ↓
MemNet.SearchAsync
  → Embedder(question)
  → 向量相似度检索
  → 返回 TopK 记忆
      ↓
拼成 memory 字符串
      ↓
LLM(prompt = memory + question)
      ↓
生成回答
      ↓
MemNet.AddAsync
  → Embedder(question)
  → 存储为新记忆

这就是一个典型的「语义记忆增强聊天」流程。

3.2. 代码细节说明

代码本质上是在做一个**“带记忆的命令行聊天（stateful chat）”**：

用 MemNet 把你每次输入的内容存成“记忆”（并做 embedding 向量化）
下次你再提问时，先从记忆库里做相似度检索，把检索到的“相关记忆”拼成一段 memory
再把 memory + 当前问题 一起喂给大模型（这里是 阿里云百炼 DashScope 的 OpenAI 兼容接口，模型是 qwen3-max）生成回答
最后把你这次的输入再写回记忆库，形成循环

下面按模块拆开讲清楚。

1) 主程序（Program.cs）在做什么

(1) 读取配置 & 注册 MemNet

var configuration = new ConfigurationBuilder()
    .AddJsonFile("appsettings.json")
    .AddEnvironmentVariables()
    .Build();

var services = new ServiceCollection();
services.AddMemNet(configuration);

从 appsettings.json + 环境变量加载配置
services.AddMemNet(configuration)：把 MemNet 相关的服务（包括 IMemoryService、配置对象等）注册到 DI 容器

你配置文件里 MemNet:Embedder 和 MemNet:LLM 的 endpoint/model/apiKey，就是给 MemNet 用的（embedding / LLM）。

(2) 创建服务、拿到 IMemoryService，并初始化

var memoryService = serviceProvider.GetRequiredService<IMemoryService>();
var memConfig = serviceProvider.GetRequiredService<IOptions<MemoryConfig>>().Value;
await memoryService.InitializeAsync();

IMemoryService：MemNet 的核心服务，提供 SearchAsync / AddAsync / InitializeAsync 等能力
InitializeAsync()：通常会做：
- 初始化存储（本地文件/数据库/向量索引等，取决于 MemNet 配置）
- 初始化向量索引结构
- 准备 embedder/模型调用组件

(3) 创建“回答生成器” CompleteChatClient

var chatApiKey = memConfig.LLM.ApiKey;
var textGenEndpoint = memConfig.LLM.Endpoint;
var extGenDeploymentName = memConfig.LLM.Model;
var completeChatClient = new CompleteChatClient(textGenEndpoint, extGenDeploymentName, chatApiKey);

这里的 CompleteChatClient 是你自己写的一个小封装，用来调用 OpenAI SDK 风格的 ChatCompletion
注意：你这里用的是 memConfig.LLM（也就是配置里 MemNet:LLM）而不是 MemNet:Embedder

(4) 进入循环：提问 → 检索记忆 → 拼 memory → 调 LLM → 写入记忆

while (true)
{
    Console.Write("You：");
    string question = Console.ReadLine();

    var searchResults = await memoryService.SearchAsync(new SearchMemoryRequest
    {
        Query = question,
        UserId = "user001"
    });

    string memory = string.Join('\n', searchResults.Select(e => e.Memory.UpdatedAt?.ToString() + e.Memory.Data));
    Console.WriteLine("Memory:");
    Console.WriteLine(memory);

    var answer = await completeChatClient.GenerateTextAsync(question, memory);
    Console.Write("AI：");
    Console.WriteLine(answer);

    await memoryService.AddAsync(new AddMemoryRequest
    {
        Messages =
        [
            new MessageContent { Role = "User", Content = question }
        ],
        UserId = "user001"
    });
}

A. SearchAsync：为什么能“搜到相关记忆”？

MemNet 典型流程是：

对 Query（question）做 embedding（用你配置的 text-embedding-v4 + endpoint + apiKey）
在向量库里做 ANN / 相似度检索（cosine / dot 等）
返回最相似的 N 条 memory（含 Data、UpdatedAt 等）

所以 searchResults 就是“跟当前问题语义最接近的历史内容”。

B. 拼接 memory：你是怎么喂给模型的？

你把检索结果拼成这段：

UpdatedAt + Data
UpdatedAt + Data
...

这段字符串就是所谓的“上下文记忆”，会放进 system message。

C. GenerateTextAsync：LLM 怎么用 memory？

你后面调用时其实是：

System message：要求模型 “根据提供的信息回答”，并把 memory 放进去
User message：当前输入 question

这就实现了“RAG-ish 的记忆增强”（更像短记忆检索，不是知识库文档分块那种严谨 RAG）。

D. AddAsync：你真正存进记忆库的是什么？

你只存了：

Role = “User”
Content = question

也就是说：你没有把 AI 的回答存进去。

结果会是：

记忆库里只会积累用户问题（而不是对话来回）
后续检索出来的“记忆”也基本都是历史问题本身

如果你期望“像 ChatGPT 一样记住对话”，通常要把 Assistant 的回答也写进去，或者至少写入“结构化摘要”。

2) CompleteChatClient 在做什么

(1) GenerateTextAsync（非流式）

ChatClient client = new(
  credential: new ApiKeyCredential(apiKey),
  model: deploymentName,
  options: new OpenAIClientOptions { Endpoint = new Uri($"{endpoint}") });

ChatCompletion completion = await client.CompleteChatAsync(
[
  new SystemChatMessage($"... memory:\n{memory}。"),
  new UserChatMessage(input)
]);

这是标准的 Chat Completions 调用
endpoint 设成 https://dashscope.aliyuncs.com/compatible-mode/v1/，说明走的是OpenAI Compatible API
model 用 qwen3-max
返回 completion.Content（可能多个 content part），你把每段 Text 拼起来返回

(2) GenerateStreamingTextAsync（流式）

var asyncCollectionResult = client.CompleteChatStreamingAsync([...]);

await foreach (var update in asyncCollectionResult)
  foreach (var contentPart in update.ContentUpdate)
    yield return contentPart.Text ?? string.Empty;

用流式接口逐步 yield token（或文本片段）
适合边生成边显示（你主程序目前没用这个方法）

3) 你的配置文件代表什么

{
  "MemNet": {
    "Embedder": {
      "Endpoint": "https://dashscope.aliyuncs.com/compatible-mode/v1/",
      "Model": "text-embedding-v4",
      "apiKey":"..."
    },
    "LLM": {
      "Endpoint": "https://dashscope.aliyuncs.com/compatible-mode/v1/",
      "Model": "qwen3-max",
      "apiKey": "..."
    }
  }
}

Embedder：给 MemNet 用来把文本转成向量（embedding）
LLM：给你 CompleteChatClient 用来生成回答（chat completion）

4. 开启http监控的效果

[HttpMataki.NET.Auto] HttpClient automatic interception started
2026-02-02 11:22:07 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"test","model":"text-embedding-v4"}

2026-02-02 11:22:10 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: d5b0f6b7-ca95-95fa-87f7-c0efb120ea63

  x-dashscope-call-gateway: true

  req-cost-time: 146

  req-arrive-time: 1770002528701

  resp-start-time: 1770002528848

  x-envoy-upstream-service-time: 146

  Date: Mon, 02 Feb 2026 03:22:08 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 21879

Body: 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**************************************************

You：我叫蔡名洋
2026-02-02 11:22:16 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u6211\u53EB\u8521\u540D\u6D0B","model":"text-embedding-v4"}

2026-02-02 11:22:17 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: 8c58c21d-2d2f-910d-865e-7982e36c6706

  x-dashscope-call-gateway: true

  req-cost-time: 127

  req-arrive-time: 1770002535477

  resp-start-time: 1770002535605

  x-envoy-upstream-service-time: 127

  Date: Mon, 02 Feb 2026 03:22:15 GMT

  Server: istio-envoy

  Content-Type: application/json

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**************************************************

Memory:

2026-02-02 11:22:17 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

  Accept: application/json

  User-Agent: OpenAI/2.4.0, (.NET 9.0.11; Microsoft Windows 10.0.26100)

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json

Body: {"messages":[{"role":"system","content":"Respond to the user's question based on the provided information. Below is the memory associated with this user：\n。"},{"role":"user","content":"我叫蔡名洋"}],"model":"qwen3-max"}

2026-02-02 11:22:22 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: b801c486-8446-9665-b058-4d479e1d1f8b

  x-dashscope-call-gateway: true

  req-cost-time: 733

  req-arrive-time: 1770002539790

  resp-start-time: 1770002540524

  x-envoy-upstream-service-time: 733

  Date: Mon, 02 Feb 2026 03:22:20 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 402

Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"你好，蔡名洋！很高兴认识你。有什么我可以帮你的吗？","role":"assistant"}}],"created":1770002539,"id":"chatcmpl-b801c486-8446-9665-b058-4d479e1d1f8b","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":16,"prompt_tokens":40,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":56}}

**************************************************

AI：你好，蔡名洋！很高兴认识你。有什么我可以帮你的吗？

2026-02-02 11:22:22 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"model":"qwen3-max","messages":[{"role":"system","content":"You are a memory extraction expert. Extract key facts, preferences, and important context from the conversation.\nReturn a JSON object with this structure:\n{\n    \u0022memories\u0022: [\n        {\u0022data\u0022: \u0022extracted fact 1\u0022},\n        {\u0022data\u0022: \u0022extracted fact 2\u0022}\n    ]\n}\n\nOnly extract factual statements, user preferences, personal information, and important context.\nBe concise and specific. Each memory should be a standalone fact."},{"role":"user","content":"User: \u6211\u53EB\u8521\u540D\u6D0B"}],"response_format":{"type":"json_object"}}

2026-02-02 11:22:25 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: 774d46d8-b795-9594-8a82-46f5b6df3a88

  x-dashscope-call-gateway: true

  req-cost-time: 820

  req-arrive-time: 1770002542946

  resp-start-time: 1770002543767

  x-envoy-upstream-service-time: 820

  Date: Mon, 02 Feb 2026 03:22:23 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 414

Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"{\n    \"memories\": [\n        {\"data\": \"用户的名字是蔡名洋\"}\n    ]\n}","role":"assistant"}}],"created":1770002543,"id":"chatcmpl-774d46d8-b795-9594-8a82-46f5b6df3a88","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":22,"prompt_tokens":111,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":133}}

**************************************************

2026-02-02 11:22:25 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u7528\u6237\u7684\u540D\u5B57\u662F\u8521\u540D\u6D0B","model":"text-embedding-v4"}

2026-02-02 11:22:27 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: 6efba2d3-8fc1-9564-b80e-d98694a246b1

  x-dashscope-call-gateway: true

  req-cost-time: 188

  req-arrive-time: 1770002545483

  resp-start-time: 1770002545672

  x-envoy-upstream-service-time: 188

  Date: Mon, 02 Feb 2026 03:22:25 GMT

  Server: istio-envoy

  Content-Type: application/json

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**************************************************

You：我今年32岁了
2026-02-02 11:22:28 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u6211\u4ECA\u5E7432\u5C81\u4E86","model":"text-embedding-v4"}

2026-02-02 11:22:30 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: 7d8a5af3-b16a-989c-ae31-c287ff7e99a1

  x-dashscope-call-gateway: true

  req-cost-time: 141

  req-arrive-time: 1770002548364

  resp-start-time: 1770002548505

  x-envoy-upstream-service-time: 140

  Date: Mon, 02 Feb 2026 03:22:28 GMT

  Server: istio-envoy

  Content-Type: application/json

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2026-02-02 11:22:30 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

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Body: {"model":"qwen3-max","messages":[{"role":"system","content":"Given the query: \u0022\u6211\u4ECA\u5E7432\u5C81\u4E86\u0022\n\nRank these memories by relevance (most relevant first):\n0. \u7528\u6237\u7684\u540D\u5B57\u662F\u8521\u540D\u6D0B\n\nReturn a JSON object with this structure:\n{\n    \u0022ranked_indices\u0022: [0, 2, 1, ...]\n}\n\nOnly return indices of relevant memories, omit irrelevant ones."}],"response_format":{"type":"json_object"}}

2026-02-02 11:22:34 - Response:

Status Code: 200 OK

Headers:

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  X-Request-ID: 652a2cee-4815-94ef-a636-06ab9981b1b9

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  Date: Mon, 02 Feb 2026 03:22:32 GMT

  Server: istio-envoy

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Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"{\n    \"ranked_indices\": []\n}","role":"assistant"}}],"created":1770002549,"id":"chatcmpl-652a2cee-4815-94ef-a636-06ab9981b1b9","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":9,"prompt_tokens":84,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":93}}

**************************************************

Memory:

2026-02-02 11:22:34 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

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Body: {"messages":[{"role":"system","content":"Respond to the user's question based on the provided information. Below is the memory associated with this user：\n。"},{"role":"user","content":"我今年32岁了"}],"model":"qwen3-max"}

2026-02-02 11:22:38 - Response:

Status Code: 200 OK

Headers:

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  Date: Mon, 02 Feb 2026 03:22:36 GMT

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Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"好的，你今年32岁。有什么我可以帮你的吗？比如关于健康、职业发展、生活规划，或者其他方面的问题？","role":"assistant"}}],"created":1770002555,"id":"chatcmpl-8c5d712a-d0ab-9b3d-a022-45e214326765","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":28,"prompt_tokens":41,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":69}}

**************************************************

AI：好的，你今年32岁。有什么我可以帮你的吗？比如关于健康、职业发展、生活规划，或者其他方面的问题？

2026-02-02 11:22:38 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

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  Content-Type: application/json; charset=utf-8

Body: {"model":"qwen3-max","messages":[{"role":"system","content":"You are a memory extraction expert. Extract key facts, preferences, and important context from the conversation.\nReturn a JSON object with this structure:\n{\n    \u0022memories\u0022: [\n        {\u0022data\u0022: \u0022extracted fact 1\u0022},\n        {\u0022data\u0022: \u0022extracted fact 2\u0022}\n    ]\n}\n\nOnly extract factual statements, user preferences, personal information, and important context.\nBe concise and specific. Each memory should be a standalone fact."},{"role":"user","content":"User: \u6211\u4ECA\u5E7432\u5C81\u4E86"}],"response_format":{"type":"json_object"}}

2026-02-02 11:22:41 - Response:

Status Code: 200 OK

Headers:

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  Date: Mon, 02 Feb 2026 03:22:39 GMT

  Server: istio-envoy

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Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"{\n    \"memories\": [\n        {\"data\": \"用户今年32岁\"}\n    ]\n}","role":"assistant"}}],"created":1770002556,"id":"chatcmpl-94708680-f278-97d7-ab85-3e734c1af8bd","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":21,"prompt_tokens":112,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":133}}

**************************************************

2026-02-02 11:22:41 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u7528\u6237\u4ECA\u5E7432\u5C81","model":"text-embedding-v4"}

2026-02-02 11:22:42 - Response:

Status Code: 200 OK

Headers:

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  Date: Mon, 02 Feb 2026 03:22:40 GMT

  Server: istio-envoy

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**************************************************

You：今年是2026年02月02日，请问我是哪一年出生的，我姓什么？
2026-02-02 11:22:49 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u4ECA\u5E74\u662F2026\u5E7402\u670802\u65E5\uFF0C\u8BF7\u95EE\u6211\u662F\u54EA\u4E00\u5E74\u51FA\u751F\u7684\uFF0C\u6211\u59D3\u4EC0\u4E48\uFF1F","model":"text-embedding-v4"}

2026-02-02 11:22:50 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: 2e712c2b-bcc8-97e7-9d97-e99ab7233f1d

  x-dashscope-call-gateway: true

  req-cost-time: 139

  req-arrive-time: 1770002569193

  resp-start-time: 1770002569333

  x-envoy-upstream-service-time: 139

  Date: Mon, 02 Feb 2026 03:22:49 GMT

  Server: istio-envoy

  Content-Type: application/json

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**************************************************

2026-02-02 11:22:50 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"model":"qwen3-max","messages":[{"role":"system","content":"Given the query: \u0022\u4ECA\u5E74\u662F2026\u5E7402\u670802\u65E5\uFF0C\u8BF7\u95EE\u6211\u662F\u54EA\u4E00\u5E74\u51FA\u751F\u7684\uFF0C\u6211\u59D3\u4EC0\u4E48\uFF1F\u0022\n\nRank these memories by relevance (most relevant first):\n0. \u7528\u6237\u4ECA\u5E7432\u5C81\n1. \u7528\u6237\u7684\u540D\u5B57\u662F\u8521\u540D\u6D0B\n\nReturn a JSON object with this structure:\n{\n    \u0022ranked_indices\u0022: [0, 2, 1, ...]\n}\n\nOnly return indices of relevant memories, omit irrelevant ones."}],"response_format":{"type":"json_object"}}

2026-02-02 11:22:52 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: 930217d9-886c-98f9-8537-1ab800bbeba1

  x-dashscope-call-gateway: true

  req-cost-time: 808

  req-arrive-time: 1770002569664

  resp-start-time: 1770002570473

  x-envoy-upstream-service-time: 808

  Date: Mon, 02 Feb 2026 03:22:50 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 365

Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"{\n    \"ranked_indices\": [0, 1]\n}","role":"assistant"}}],"created":1770002569,"id":"chatcmpl-930217d9-886c-98f9-8537-1ab800bbeba1","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":14,"prompt_tokens":111,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":125}}

**************************************************

Memory:
用户今年32岁
用户的名字是蔡名洋
2026-02-02 11:22:52 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

  Accept: application/json

  User-Agent: OpenAI/2.4.0, (.NET 9.0.11; Microsoft Windows 10.0.26100)

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json

Body: {"messages":[{"role":"system","content":"Respond to the user's question based on the provided information. Below is the memory associated with this user：\n用户今年32岁\n用户的名字是蔡名洋。"},{"role":"user","content":"今年是2026年02月02日，请问我是哪一年出生的，我姓什么？"}],"model":"qwen3-max"}

2026-02-02 11:22:59 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: c3128886-7c40-9290-a26d-2dc9f4b1dae8

  x-dashscope-call-gateway: true

  req-cost-time: 6657

  req-arrive-time: 1770002570786

  resp-start-time: 1770002577443

  x-envoy-upstream-service-time: 6656

  Date: Mon, 02 Feb 2026 03:22:56 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 1185

Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"根据你提供的信息：\n\n- 今天是2026年2月2日  \n- 你今年32岁  \n\n那么你的出生年份是：  \n**2026 - 32 = 1994年**\n\n但要注意，如果你的生日还没到（即在2026年2月2日之后才满33岁），那你实际上出生于 **1993年**。  \n由于今天是2月2日，如果你的生日在2月2日或之前，你已经满32岁，出生年份就是 **1994年**；如果生日在2月2日之后，那你目前还是31岁，要到今年生日才满32岁，因此出生年份是 **1993年**。\n\n不过，你提到“用户今年32岁”，通常是指当前已满32岁，所以我们可以合理推断你是 **1993年出生**（因为到2026年2月2日已满32岁，说明生日已过）。\n\n再结合你的名字是 **蔡名洋**，所以你姓 **蔡**。\n\n? 答案：  \n- 你出生于 **1993年**  \n- 你姓 **蔡**","role":"assistant"}}],"created":1770002570,"id":"chatcmpl-c3128886-7c40-9290-a26d-2dc9f4b1dae8","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":270,"prompt_tokens":72,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":342}}

**************************************************

AI：根据你提供的信息：

- 今天是2026年2月2日
- 你今年32岁

那么你的出生年份是：
**2026 - 32 = 1994年**

但要注意，如果你的生日还没到（即在2026年2月2日之后才满33岁），那你实际上出生于 **1993年**。
由于今天是2月2日，如果你的生日在2月2日或之前，你已经满32岁，出生年份就是 **1994年**；如果生日在2月2日之后，那你目前还是31岁，要到今年生日才满32岁，因此出生年份是 **1993年**。

不过，你提到“用户今年32岁”，通常是指当前已满32岁，所以我们可以合理推断你是 **1993年出生**（因为到2026年2月2日已满32岁，说明生日已过）。

再结合你的名字是 **蔡名洋**，所以你姓 **蔡**。

? 答案：
- 你出生于 **1993年**
- 你姓 **蔡**

2026-02-02 11:22:59 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"model":"qwen3-max","messages":[{"role":"system","content":"You are a memory extraction expert. Extract key facts, preferences, and important context from the conversation.\nReturn a JSON object with this structure:\n{\n    \u0022memories\u0022: [\n        {\u0022data\u0022: \u0022extracted fact 1\u0022},\n        {\u0022data\u0022: \u0022extracted fact 2\u0022}\n    ]\n}\n\nOnly extract factual statements, user preferences, personal information, and important context.\nBe concise and specific. Each memory should be a standalone fact."},{"role":"user","content":"User: \u4ECA\u5E74\u662F2026\u5E7402\u670802\u65E5\uFF0C\u8BF7\u95EE\u6211\u662F\u54EA\u4E00\u5E74\u51FA\u751F\u7684\uFF0C\u6211\u59D3\u4EC0\u4E48\uFF1F"}],"response_format":{"type":"json_object"}}

2026-02-02 11:23:01 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: a453384b-d3e6-9a1c-a764-8f4ae185e379

  x-dashscope-call-gateway: true

  req-cost-time: 1543

  req-arrive-time: 1770002578449

  resp-start-time: 1770002579992

  x-envoy-upstream-service-time: 1542

  Date: Mon, 02 Feb 2026 03:22:59 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 521

Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"{\n    \"memories\": [\n        {\"data\": \"用户在2026年02月02日提问\"},\n        {\"data\": \"用户未提供出生年份\"},\n        {\"data\": \"用户未提供姓氏\"}\n    ]\n}","role":"assistant"}}],"created":1770002578,"id":"chatcmpl-a453384b-d3e6-9a1c-a764-8f4ae185e379","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":53,"prompt_tokens":131,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":184}}

**************************************************

2026-02-02 11:23:01 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u7528\u6237\u57282026\u5E7402\u670802\u65E5\u63D0\u95EE","model":"text-embedding-v4"}

2026-02-02 11:23:02 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: fe8d1c41-732b-966b-b2a9-325737b2cf45

  x-dashscope-call-gateway: true

  req-cost-time: 144

  req-arrive-time: 1770002580301

  resp-start-time: 1770002580445

  x-envoy-upstream-service-time: 144

  Date: Mon, 02 Feb 2026 03:23:00 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 21864

Body: 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**************************************************

2026-02-02 11:23:02 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u7528\u6237\u672A\u63D0\u4F9B\u51FA\u751F\u5E74\u4EFD","model":"text-embedding-v4"}

2026-02-02 11:23:02 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: b070139d-4ba9-94d5-9d50-b066d0f75166

  x-dashscope-call-gateway: true

  req-cost-time: 214

  req-arrive-time: 1770002580809

  resp-start-time: 1770002581023

  x-envoy-upstream-service-time: 214

  Date: Mon, 02 Feb 2026 03:23:00 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 21830

Body: 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**************************************************

2026-02-02 11:23:02 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u7528\u6237\u672A\u63D0\u4F9B\u59D3\u6C0F","model":"text-embedding-v4"}

2026-02-02 11:23:03 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: d320f4f7-fc05-9c42-8634-61e271326e15

  x-dashscope-call-gateway: true

  req-cost-time: 147

  req-arrive-time: 1770002581348

  resp-start-time: 1770002581496

  x-envoy-upstream-service-time: 147

  Date: Mon, 02 Feb 2026 03:23:01 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 21897

Body: 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**************************************************

2026-02-02 11:23:03 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"model":"qwen3-max","messages":[{"role":"system","content":"You are a memory merging expert. You can merge two memory statements into one coherent, accurate statement.\n\nRules:\n1. Preserve all factual information from both.\n2. If they conflict, prefer the newer information.\n3. Merge them into a concise, and non-redundant factual summary, keeping all unique information.\n4. Return ONLY the merged memory text, no explanation.\n5. All of \u0022I\u0022, \u0022User\u0022, \u0022My\u0022 and \u0022Me\u0022 refer to the user.\n\nExample:\nExisting Memory: I love jogging; \nNew Memory: My interest in jogging.\nOutput: I love jogging."},{"role":"user","content":"Existing Memory: \u0022\u7528\u6237\u4ECA\u5E7432\u5C81\u0022\n\nNew Memory: \u0022\u7528\u6237\u672A\u63D0\u4F9B\u51FA\u751F\u5E74\u4EFD\u0022"}]}

2026-02-02 11:23:04 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: 6a6fc094-c736-95f7-93b7-45155189ad04

  x-dashscope-call-gateway: true

  req-cost-time: 617

  req-arrive-time: 1770002581827

  resp-start-time: 1770002582445

  x-envoy-upstream-service-time: 617

  Date: Mon, 02 Feb 2026 03:23:02 GMT

  Server: istio-envoy

  Content-Type: application/json

  Content-Length: 348

Body: {"choices":[{"finish_reason":"stop","index":0,"message":{"content":"用户今年32岁。","role":"assistant"}}],"created":1770002581,"id":"chatcmpl-6a6fc094-c736-95f7-93b7-45155189ad04","model":"qwen3-max","object":"chat.completion","usage":{"completion_tokens":6,"prompt_tokens":156,"prompt_tokens_details":{"cached_tokens":0},"total_tokens":162}}

**************************************************

2026-02-02 11:23:04 - Request:

Method: POST

URL: https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings

Headers:

  Authorization: Bearer sk-1c76f00d05f44099a60300d6a78d0ac9

  Content-Type: application/json; charset=utf-8

Body: {"input":"\u7528\u6237\u4ECA\u5E7432\u5C81\u3002","model":"text-embedding-v4"}

2026-02-02 11:23:05 - Response:

Status Code: 200 OK

Headers:

  Vary: Origin, Access-Control-Request-Method, Access-Control-Request-Headers, Accept-Encoding

  X-Request-ID: 8dfcd6ed-5043-91b6-a544-6d9a6e9a60f5

  x-dashscope-call-gateway: true

  req-cost-time: 150

  req-arrive-time: 1770002583305

  resp-start-time: 1770002583456

  x-envoy-upstream-service-time: 150

  Date: Mon, 02 Feb 2026 03:23:03 GMT

  Server: istio-envoy

  Content-Type: application/json

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