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基于 Microsoft Agent Framework 实现第三方聊天历史存储

理解 Microsoft Agent Framework

Microsoft Agent Framework 是一个用于构建对话式 AI 应用的框架，支持自然语言处理和上下文管理。其核心组件包括 Bot Framework SDK 和 Azure Bot Service，但默认聊天历史通常存储在 Azure 服务中。

具体实现可参考NetCoreKevin的kevin.AI.AgentFramework和KevinAIChatMessageStore服务模块

基于NET构建的现代化AI智能体Saas企业级架构：

项目地址：github：https://github.com/junkai-li/NetCoreKevin
Gitee: https://gitee.com/netkevin-li/NetCoreKevin

配置自定义存储提供程序

1. **实现ChatMessageStore**
   创建自定义存储类继承Microsoft.Bot.Builder.IStorage，需实现以下方法：

   publicsealedclassKevinChatMessageStore:ChatMessageStore{privateIKevinAIChatMessageStore_chatMessageStore;publicstringThreadDbKey{get;privateset;}publicKevinChatMessageStore(IKevinAIChatMessageStorevectorStore,JsonElementserializedStoreState,stringAIChatsId,JsonSerializerOptions?jsonSerializerOptions=null){this._chatMessageStore=vectorStore??thrownewArgumentNullException(nameof(vectorStore));this.ThreadDbKey=AIChatsId;}publicoverrideasyncTaskAddMessagesAsync(IEnumerable<ChatMessage>messages,CancellationTokencancellationToken){await_chatMessageStore.AddMessagesAsync(messages.Select(x=>newChatHistoryItemDto(){Key=this.ThreadDbKey+x.MessageId,Timestamp=DateTimeOffset.UtcNow,ThreadId=this.ThreadDbKey,MessageId=x.MessageId,Role=x.Role.Value,SerializedMessage=JsonSerializer.Serialize(x),MessageText=x.Text}).ToList(),cancellationToken);// 设置前景色为红色// 保存原始颜色，以便之后恢复ConsoleColororiginalColor=Console.ForegroundColor;Console.ForegroundColor=ConsoleColor.Red;Console.WriteLine("聊天消息记录:",Color.Red);messages.Select(x=>x.Text).ToList().ForEach(t=>Console.WriteLine(t));// 设置前景色为红色Console.ForegroundColor=ConsoleColor.Red;Console.WriteLine("聊天消息记录添加完成",Color.Red);// 恢复原始颜色Console.ForegroundColor=originalColor;}publicoverrideasyncTask<IEnumerable<ChatMessage>>GetMessagesAsync(CancellationTokencancellationToken){vardata=await_chatMessageStore.GetMessagesAsync(this.ThreadDbKey,cancellationToken);varmessages=data.ConvertAll(x=>JsonSerializer.Deserialize<ChatMessage>(x.SerializedMessage!)!);messages.Reverse();ConsoleColororiginalColor=Console.ForegroundColor;Console.ForegroundColor=ConsoleColor.Red;Console.WriteLine("所有聊天消息记录开始:",Color.Red);messages.Select(x=>x.Text).ToList().ForEach(t=>Console.WriteLine(t));Console.WriteLine("所有聊天消息记录结束:",Color.Red);// 恢复原始颜色Console.ForegroundColor=originalColor;returnmessages;}publicoverrideJsonElementSerialize(JsonSerializerOptions?jsonSerializerOptions=null)=>// We have to serialize the thread id, so that on deserialization you can retrieve the messages using the same thread id.JsonSerializer.SerializeToElement(this.ThreadDbKey);}

2. 实现IKevinAIChatMessageStore

   TaskAddMessagesAsync(List<ChatHistoryItemDto>chatHistoryItems,CancellationTokencancellationToken);Task<List<ChatHistoryItemDto>>GetMessagesAsync(stringthreadId,CancellationTokencancellationToken);

3. 实现注入到AI中间件中
   1.定义AI服务：

   /// <summary>/// AI服务/// </summary>publicclassAIAgentService:IAIAgentService{publicAIAgentService(){}publicasyncTask<AIAgent>CreateOpenAIAgent(stringname,stringprompt,stringdescription,stringurl,stringmodel,stringkeySecret,IList<AITool>?tools=null,ChatResponseFormat?chatResponseFormat=null,Func<IChatClient,IChatClient>?clientFactory=null,ILoggerFactory?loggerFactory=null,IServiceProvider?services=null){OpenAIClientOptionsopenAIClientOptions=newOpenAIClientOptions();openAIClientOptions.Endpoint=newUri(url);varai=newOpenAIClient(newApiKeyCredential(keySecret),openAIClientOptions);if(chatResponseFormat!=default){ChatOptionschatOptions=new(){ResponseFormat=chatResponseFormat};returnai.GetChatClient(model).CreateAIAgent(newChatClientAgentOptions(){Name=name,Instructions=prompt,ChatOptions=chatOptions,Description=description});}returnai.GetChatClient(model).CreateAIAgent(instructions:prompt,name:name,prompt,tools,clientFactory,loggerFactory,services);}publicasyncTask<AIAgent>CreateOpenAIAgent(stringmsg,stringurl,stringmodel,stringkeySecret,ChatClientAgentOptionschatClientAgentOptions){OpenAIClientOptionsopenAIClientOptions=newOpenAIClientOptions();openAIClientOptions.Endpoint=newUri(url);varai=newOpenAIClient(newApiKeyCredential(keySecret),openAIClientOptions);returnai.GetChatClient(model).CreateAIAgent(chatClientAgentOptions);}/// <summary>/// 智能体转换为McpServerTool/// </summary>/// <param name="aIAgent">智能体</param>/// <returns></returns>/// <exception cref="NotImplementedException"></exception>publicMcpServerToolAIAgentAsMcpServerTool(AIAgentaIAgent){returnMcpServerTool.Create(aIAgent.AsAIFunction());}/// <summary>/// 获取代理/// </summary>/// <returns></returns>publicIChatClientGetChatClient(stringurl,stringmodel,stringkeySecret){OpenAIClientOptionsopenAIClientOptions=newOpenAIClientOptions();openAIClientOptions.Endpoint=newUri(url);varai=newOpenAIClient(newApiKeyCredential(model),openAIClientOptions);returnai.GetChatClient(keySecret).AsIChatClient();}publicasyncTask<(AIAgent,AgentRunResponse)>CreateOpenAIAgentAndSendMSG(stringmsg,stringname,stringprompt,stringdescription,stringurl,stringmodel,stringkeySecret,IList<AITool>?tools=null,ChatResponseFormat?chatResponseFormat=null,Func<IChatClient,IChatClient>?clientFactory=null,ILoggerFactory?loggerFactory=null,IServiceProvider?services=null){OpenAIClientOptionsopenAIClientOptions=newOpenAIClientOptions();openAIClientOptions.Endpoint=newUri(url);varai=newOpenAIClient(newApiKeyCredential(keySecret),openAIClientOptions);varaiAgent=ai.GetChatClient(model).CreateAIAgent(instructions:prompt,name:name,prompt,tools,clientFactory,loggerFactory,services);if(chatResponseFormat!=default){ChatOptionschatOptions=new(){ResponseFormat=chatResponseFormat};aiAgent=ai.GetChatClient(model).CreateAIAgent(newChatClientAgentOptions(){Name=name,Instructions=prompt,ChatOptions=chatOptions,Description=description});}varreslut=awaitaiAgent.RunAsync(msg);return(aiAgent,reslut);}publicasyncTask<(AIAgent,AgentRunResponse)>CreateOpenAIAgentAndSendMSG(stringmsg,stringurl,stringmodel,stringkeySecret,ChatClientAgentOptionschatClientAgentOptions){OpenAIClientOptionsopenAIClientOptions=newOpenAIClientOptions();openAIClientOptions.Endpoint=newUri(url);varai=newOpenAIClient(newApiKeyCredential(keySecret),openAIClientOptions);varaiAgent=ai.GetChatClient(model).CreateAIAgent(chatClientAgentOptions);varreslut=awaitaiAgent.RunAsync(msg);return(aiAgent,reslut);}}

2.使用AI服务

addAi.Content=(awaitaIAgentService.CreateOpenAIAgentAndSendMSG(add.Content,aIModels.EndPoint,aIModels.ModelName,aIModels.ModelKey,newChatClientAgentOptions{Name=aiapp.Name,Instructions=aIPrompts.Prompt,Description=aIPrompts.Description??"你是一个智能体,请根据你的提示词进行相关回答",ChatMessageStoreFactory=ctx=>{// Create a new chat message store for this agent that stores the messages in a vector store.returnnewKevinChatMessageStore(kevinAIChatMessageStore,ctx.SerializedState,par.AIChatsId.ToString(),ctx.JsonSerializerOptions);}})).Item2.Text;

数据库设计建议

对于关系型数据库（如 SQL Server），建议的表结构：

/// <summary>/// 专门用于存储AI聊天记录的表/// </summary>[Table("TAIChatMessageStore")][Description("专门用于存储AI聊天记录的表")][Index(nameof(Key))][Index(nameof(ThreadId))][Index(nameof(Role))][Index(nameof(MessageId))]publicclass TAIChatMessageStore : CUD_User {[MaxLength(200)]publicstringKey{ get;set;}[MaxLength(100)]publicstring ThreadId { get;set;}[Description("消息时间stamp")]publicDateTimeOffset?Timestamp{ get;set;}/// <summary>/// 角色标识/// </summary>[MaxLength(50)]publicstring Role { get;set;}publicstring SerializedMessage { get;set;}/// <summary>/// 消息内容/// </summary>publicstring? MessageText { get;set;}/// <summary>/// 消息id/// </summary>[Description("消息id")][MaxLength(100)]publicstring? MessageId { get;set;} }

实现数据持久化

1. 写入示例
   使用 Entity Framework Core 保存数据：

   publicasyncTaskAddMessagesAsync(List<ChatHistoryItemDto>chatHistoryItems,CancellationTokencancellationToken){varadddata=chatHistoryItems.Select(t=>newTAIChatMessageStore{Id=SnowflakeIdService.GetNextId(),CreateTime=DateTime.Now,CreateUserId=CurrentUser.UserId,IsDelete=false,TenantId=CurrentUser.TenantId,ThreadId=t.ThreadId,Timestamp=t.Timestamp,Role=t.Role,Key=t.Key,SerializedMessage=t.SerializedMessage,MessageText=t.MessageText,MessageId=t.MessageId}).ToList();aIChatMessageStoreRp.AddRange(adddata);awaitaIChatMessageStoreRp.SaveChangesAsync();}

2. 读取示例

   publicTask<List<ChatHistoryItemDto>>GetMessagesAsync(stringthreadId,CancellationTokencancellationToken){returnaIChatMessageStoreRp.Query().Where(t=>t.ThreadId==threadId&&t.IsDelete==false).Select(t=>newChatHistoryItemDto{Key=t.Key,ThreadId=t.ThreadId,Timestamp=t.Timestamp,SerializedMessage=t.SerializedMessage,MessageText=t.MessageText,Role=t.Role,MessageId=t.MessageId}).ToListAsync();}

性能优化建议

• 为高频查询字段（如UserId和ChannelId）添加索引
• 实现数据分片策略应对大规模历史记录
• 考虑使用 Redis 缓存热点对话数据

安全注意事项

• 加密存储敏感对话内容
• 实现数据保留策略定期清理旧记录
• 遵守 GDPR 等数据隐私法规

测试验证方法

1. 编写单元测试验证存储接口实现
2. 使用 Bot Framework Emulator 进行端到端测试
3. 进行负载测试验证性能表现

扩展可能性

1. 添加全文检索支持（如 Azure Cognitive Search）
2. 实现跨渠道对话历史同步
3. 开发分析模块生成对话洞察报告

这种实现方式允许完全控制数据存储位置和格式，同时保持与 Bot Framework 的兼容性。根据具体需求可选择 SQL Server、Cosmos DB 或其他数据库解决方案。