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【BotSharp框架示例 ——实现聊天机器人，并通过 DeepSeek V3实现 function calling】

news 来源：原创 2025/7/14 7:46:43

BotSharp框架示例 ——实现聊天机器人，并通过 DeepSeek V3实现 function calling

一、一点点感悟
二、创建项目
- 1、创建项目
- 2、添加引用
- 3、MyWeatherPlugin项目代码编写
- 4、WeatherApiDefaultService项目代码编写
- 5、WebAPI MyWeatherAPI 的项目代码编写
- 6、data文件夹中声明agent 、conversation、functions
三、运行程序进行测试
四、总结

本文通过仔细研究 BotSharp 框架的示例代码PizzaBot，实现以下内容：
1、分析Botsharp实现聊天机器人的基本组成部分；
2、模仿PizzaBot一步一步搭建自己的聊天机器人；
3、通过分析PizzaBot，实现控制agent 驱动不同的大模型；
4、实现大模型调用本地函数的示例。

一、一点点感悟

深刻感觉.Net在AI领域已经有很多优秀的框架可以使用，但是苦于资料太少，本人就研究这一个小小的例子，就花费了很长的时间。
真希望出现以前那种一步一步学xxx 类似的文章，能引导学习的人快速上手。
真希望本例子能够作为一个投石问路，会有更多的人来分享实际的案例，清晰的使用指南。

二、创建项目

闲言少叙，开始模仿。
本文参考
1、Botsharp的示例 PizzaBot
2、Botsharp的官方文档：https://botsharp.verdure-hiro.cn/guide/agent/hook

1、创建项目

目标是搭建一个关于天气咨询的一个聊天机器人，
所以模仿PizzaBot 创建了三个项目
1、创建类库MyWeatherPlugin（主要部分，承载AgentHook、ConversationHook，以及本地Function的定义）
2、创建类库WeatherApiDefaultService（用于模拟PizzaBot，实现默认的服务注册和方法–此处经过测试，也可以不添加，不是必须要执行的步骤）
3、创建WebAPI项目MyWeatherAPI（通过配置、启动聊天机器人，主要关注配置文件和启动类）

2、添加引用

通过测试，需要添加以下引用：
1、MyWeatherPlugin中需要添加 BotSharp.Core的引用
2、WeatherApiDefaultService 因为是非必须的，可以不用添加这个项目了，如果添加了，可以照着PizzaBot照搬下
3、MyWeatherAPI 需要添加以下类库引用：

 BotSharp.CoreBotSharp.AbstractionBotSharp.LoggerBotSharp.OpenAPIBotSharp.Plugin.ChatHub

由于本例中使用到了 DeepSeek，所以要引用

BotSharp.Plugin.DeepSeekAI

在之前的探索中还使用过 llama本地模型，如果要用llama模型的话，要引用以下的库

BotSharp.Plugin.LLamaSharp
LLamaSharp
LLamaSharp.Backend.Cpu
LLamaSharp.Backend.Cuda12

3、MyWeatherPlugin项目代码编写

模拟Pizzabot，添加以下内容
1）添加公共using文件
2）添加MyWeatherPlugin
注意，MyWeatherPlugin文件中需要注册注册一些AgentId，示例中使用的是启动后通过postman 创建agent的方法（引用见 Botsharp的官方文档：https://botsharp.verdure-hiro.cn/guide/agent/hook，有启动postman workspace的链接）

using BotSharp.Abstraction.Agents;
using BotSharp.Abstraction.Conversations;
using BotSharp.Abstraction.Plugins;
using MyWeatherPlugin.Hooks;namespace MyWeatherPlugin
{public class MyWeatherPlugin: IBotSharpPlugin{public string Id => "1c8270eb-de63-4ca0-8903-654d83ce5ece";public string Name => "MyWeather AI Assistant";public string Description => "An example of an Weather AI Chatbot.";public string IconUrl => "https://cdn-icons-png.flaticon.com/512/6978/6978255.png";public string[] AgentIds => new[]{"01fcc3e5-0af7-49e6-ad7a-a760bd12dc4d","01fcc3e5-9af7-49e6-ad7a-a760bd12dc4a","6745151e-6d46-4a02-8de4-1c4f21c7da95","dfd9b46d-d00c-40af-8a75-3fbdc2b89869"};public void RegisterDI(IServiceCollection services, IConfiguration config){// Register hooksservices.AddScoped<IAgentHook, MyWeatherAgentHook>();//services.AddScoped<IConversationService, MyWeatherConversationHook>();}}
}

3）新建 Hooks的目录，参考Pizzabot，添加agenthook和 conversationhook
CommonAgentHook
MyWeatherAgentHook
MyWeatherConversationHook
agent是一个智能体，可以决定调用某一个具体的大模型，决定使用什么provider。
conversation是一个会话，也就是一个上下文，是LLM模型都需要的一个参数，用于通过上下文来推理用户的问题。
所以这里的agentHook 是botsharp中用于注册agent和agent行为的程序，能决定 agent在创建时、加载时等各个事件发生时的一些自定义行为。
本例中没有涉及，但是在实验的时候，通过添加不同的方法，是能够验证，这些事件是能够顺利被触发的。
conversationhook 同理

代码分别是：

using BotSharp.Abstraction.Agents;namespace MyWeatherPlugin.Hooks
{public class CommonAgentHook : AgentHookBase{public override string SelfId => string.Empty;public CommonAgentHook(IServiceProvider services, AgentSettings settings): base(services, settings){}public override bool OnInstructionLoaded(string template, Dictionary<string, object> dict){dict["current_date"] = DateTime.Now.ToString("MM/dd/yyyy");dict["current_time"] = DateTime.Now.ToString("hh:mm tt");dict["current_weekday"] = DateTime.Now.DayOfWeek;return base.OnInstructionLoaded(template, dict);}}}

using BotSharp.Abstraction.Agents.Enums;
using BotSharp.Abstraction.Agents;
using BotSharp.Abstraction.Functions.Models;namespace MyWeatherPlugin.Hooks
{public class MyWeatherAgentHook : AgentHookBase{public override string SelfId => BuiltInAgentId.AIAssistant;public MyWeatherAgentHook(IServiceProvider services, AgentSettings settings): base(services, settings){}public override bool OnInstructionLoaded(string template, Dictionary<string, object> dict){return base.OnInstructionLoaded(template, dict);}}}

using BotSharp.Abstraction.Conversations.Models;
using BotSharp.Abstraction.Conversations;namespace MyWeatherPlugin.Hooks
{public class MyWeatherConversationHook : ConversationHookBase{private readonly IServiceProvider _services;private readonly IConversationStateService _states;public MyWeatherConversationHook(IServiceProvider services,IConversationStateService states){_services = services;_states = states;}public override async Task OnPostbackMessageReceived(RoleDialogModel message, PostbackMessageModel replyMsg){if (replyMsg.FunctionName == "get_my_weather_type"){// message.StopCompletion = true;}return;}public override Task OnTaskCompleted(RoleDialogModel message){return base.OnTaskCompleted(message);}#if USE_BOTSHARPpublic override async Task OnResponseGenerated(RoleDialogModel message){var agentService = _services.GetRequiredService<IAgentService>();var state = _services.GetRequiredService<IConversationStateService>();var agent = await agentService.LoadAgent(message.CurrentAgentId);if (agent.McpTools.Any(item => item.Functions.Any(x => x.Name == message.FunctionName))){var data = JsonDocument.Parse(JsonSerializer.Serialize(message.Data));state.SaveStateByArgs(data);}await base.OnResponseGenerated(message);}
#endif}}

4）添加Functions目录，参考Pizzabot 添加几个function，
这里是本例子的一个重点，这里主要是通过 function定义，决定一些在本地可以被调用到的自定义行为。这是非常重要的。本例中添加了三个方法：
GetMyWeatherTypeFn
GetWeatherDesFn（这是本例中最后实现调用的方法）
GetWeatherToolFn
方法中的 Execute 方法，决定了最终方法最终返回给大模型的文本内容

using BotSharp.Abstraction.Conversations.Models;
using BotSharp.Abstraction.Conversations;
using BotSharp.Abstraction.Messaging.Models.RichContent.Template;
using BotSharp.Abstraction.Messaging.Models.RichContent;
using BotSharp.Abstraction.Messaging;
using System.Text.Json;namespace MyWeatherPlugin.Functions
{public class GetMyWeatherTypeFn : IFunctionCallback{public string Name => "get_my_weather_type";private readonly IServiceProvider _services;public GetMyWeatherTypeFn(IServiceProvider services){_services = services;}public async Task<bool> Execute(RoleDialogModel message){var states = _services.GetRequiredService<IConversationStateService>();var weatherTypes = new List<string>{"晴天","雨天","雪天"};message.Content = JsonSerializer.Serialize(weatherTypes);message.RichContent = new RichContent<IRichMessage>{Recipient = new Recipient{Id = states.GetConversationId()},FillPostback = true,Message = new ButtonTemplateMessage{Text = "Please select a weather type",Buttons = weatherTypes.Select(x => new ElementButton{Type = "text",Title = x,Payload = x}).ToArray()}};return true;}}}

using BotSharp.Abstraction.Conversations.Models;
using System.Text.Json;namespace MyWeatherPlugin.Functions;public class GetWeatherDesFn : IFunctionCallback
{public string Name => "get_weather_des";public async Task<bool> Execute(RoleDialogModel message){message.Data = new{sunny_desc = "晴空万里，万里无云，艳阳高照，碧海蓝天",rainny_desc = "大雨倾盆，电闪雷鸣，乌云密闭，水位猛涨",snowny_desc = "鹅毛大雪，原驰蜡象，瑞雪丰年，一片雪白"};message.Content =JsonSerializer.Serialize(message.Data);return true;}
}

using BotSharp.Abstraction.Conversations;
using BotSharp.Abstraction.Conversations.Models;namespace MyWeatherPlugin.Functions;public class GetWeatherToolFn : IFunctionCallback
{public string Name => "get_weather_tool";private readonly IServiceProvider _service;public GetWeatherToolFn(IServiceProvider service){_service = service;}public async Task<bool> Execute(RoleDialogModel message){message.Content = "The weather date is 2025-04-25";var state = _service.GetRequiredService<IConversationStateService>();state.SetState("weather_date", "2025-04-25");return true;}
}

最终 MyWeatherPlugin 的结构如图：
在这里插入图片描述

4、WeatherApiDefaultService项目代码编写

因为是非必须的，而且完全参考 Pizzabot中的 defaultservice的，所以这部分就略过了

5、WebAPI MyWeatherAPI 的项目代码编写

看下配置文件： appsettings.json
这是经过实验，需要保留的一些节点，
保留 jwt节点是因为botsharp框架中涉及到用户认证
LlmProviders 中保留本例中用到LLM模型的驱动
PluginLoader 是加载的所有库
注意填上自己的 appkey

{"Logging": {"LogLevel": {"Default": "Information","Microsoft.AspNetCore": "Warning"}},"AllowedHosts": "*","AllowedOrigins": ["http://localhost:5015","http://0.0.0.0:5015","https://botsharp.scisharpstack.org","https://chat.scisharpstack.org"],"Jwt": {"Issuer": "botsharp","Audience": "botsharp","Key": "31ba6052aa6f4569901facc3a41fcb4adfd9b46dd00c40af8a753fbdc2b89869"},"LlmProviders": [{"Provider": "llama-sharp","Models": [{"Name": "llama-2-7b-guanaco-qlora.Q2_K.gguf","Type": "chat"}]},{"Provider": "deepseek-ai","Models": [{"Name": "deepseek-chat","ApiKey": "xxxxx","Endpoint": "https://api.deepseek.com/v1/","Type": "chat","PromptCost": 0.0015,"CompletionCost": 0.002}]}],"Router": {},"Evaluator": {"AgentId": "dfd9b46d-d00c-40af-8a75-3fbdc2b89869"},"Agent": {"DataDir": "agents","TemplateFormat": "liquid","HostAgentId": "01fcc3e5-0af7-49e6-ad7a-a760bd12dc4d","EnableTranslator": false,"LlmConfig": {//"Provider": "llama-sharp",//"Model": "llama-2-7b-guanaco-qlora.Q2_K.gguf""Provider": "deepseek-ai","Model": "deepseek-chat"}},"MCP": {"Enabled": false,"McpClientOptions": {"ClientInfo": {"Name": "SimpleToolsBotsharp","Version": "1.0.0"}},"McpServerConfigs": [{"Id": "WeatherServer","Name": "WeatherServer","TransportType": "sse","TransportOptions": [],"Location": "http://localhost:58905/sse"}]},"Conversation": {"DataDir": "conversations","ShowVerboseLog": false,"EnableLlmCompletionLog": false,"EnableExecutionLog": true,"EnableContentLog": true,"EnableStateLog": true,"EnableTranslationMemory": false,"CleanSetting": {"Enable": true,"BatchSize": 50,"MessageLimit": 2,"BufferHours": 12,"ExcludeAgentIds": []},"RateLimit": {"MaxConversationPerDay": 100,"MaxInputLengthPerRequest": 256,"MinTimeSecondsBetweenMessages": 2}},"SideCar": {"Conversation": {"Provider": "botsharp"}},"ChatHub": {"EventDispatchBy": "group"},"LlamaSharp": {"Interactive": true,"ModelDir": "F:/models","DefaultModel": "llama-2-7b-guanaco-qlora.Q2_K.gguf","MaxContextLength": 1024,"NumberOfGpuLayer": 20},"AzureOpenAi": {},"RoutingSpeeder": {},"Database": {"Default": "FileRepository","TablePrefix": "BotSharp","BotSharpMongoDb": "","Redis": "botsharp.redis.cache.windows.net:6380,password=,ssl=True,abortConnect=False","FileRepository": "data","Assemblies": [ "BotSharp.Core" ]},"Interpreter": {"Python": {"PythonDLL": "C:/Python313/python313.dll"}},"PluginLoader": {"Assemblies": ["BotSharp.Core","BotSharp.Core.SideCar","BotSharp.Core.Crontab",     "BotSharp.Logger",    "BotSharp.Plugin.OpenAI","BotSharp.Plugin.AzureOpenAI",     "BotSharp.Plugin.DeepSeekAI","BotSharp.Plugin.MetaMessenger","BotSharp.Plugin.ChatHub",    "MyWeatherPlugin",    "BotSharp.Plugin.LLamaSharp" ],"ExcludedFunctions": ["McpToolAdapter"]}
}

progam.cs 是启动程序，代码如下

using BotSharp.Abstraction.Conversations;
using BotSharp.Abstraction.Messaging.JsonConverters;
using BotSharp.Abstraction.Users;
using BotSharp.Core;
using BotSharp.Core.Agents;
using BotSharp.Core.MCP;
using BotSharp.Logger;
using BotSharp.OpenAPI;
using BotSharp.Plugin.ChatHub;
using MyWeatherPlugin.Hooks;var builder = WebApplication.CreateBuilder(args);// Add services to the container.builder.Services.AddBotSharpCore(builder.Configuration, options =>
{options.JsonSerializerOptions.Converters.Add(new RichContentJsonConverter());options.JsonSerializerOptions.Converters.Add(new TemplateMessageJsonConverter());
}).AddBotSharpOpenAPI(builder.Configuration,
builder.Configuration.GetSection("AllowedOrigins").Get<string[]>() ?? new[]{"http://0.0.0.0:5015","https://botsharp.scisharpstack.org","https://chat.scisharpstack.org"}, builder.Environment, true).AddBotSharpLogger(builder.Configuration);builder.Services.AddControllers();builder.AddServiceDefaults();
builder.Services.AddSignalR();
var app = builder.Build();// Configure the HTTP request pipeline.
app.MapHub<SignalRHub>("/chatHub");
app.UseMiddleware<WebSocketsMiddleware>();app.UseBotSharp().UseBotSharpOpenAPI(app.Environment);//app.MapControllers();app.Run();

6、data文件夹中声明agent 、conversation、functions

这是这个例子中最重要的部分，
1、程序运行
2、通过官方文档：https://botsharp.verdure-hiro.cn/guide/quick-start/get-started
中的 postman 链接如下：
在这里插入图片描述
调整成自己的 Host等参数

3）依次执行
New User Account
Get Access Token
Create Agent
的操作，如图：

4）这时候要查找下创建的 agent目录在哪里，应该在 webAPI的bin目录下
\bin\Debug\net9.0\data\agents
我把这个目录拷贝到 webAPI的根目录以及 Myweatherplugin项目的根目录下了
注意每次修改的时候我都同步拷贝了。（实验是有效的，可能不需要，但是这么做是不出错的）

5）假设创建的agentId是本例的 01fcc3e5-0af7-49e6-ad7a-a760bd12dc4d
注意要修改 appsettings.json 中的 agent节点，修改 MyWeatherPlugin 项目中的 MyWeatherPlugin 类中加载的 agentid数组
然后拷贝 webAPI目录下的 \MyWeatherAPI（解决方案目录）\MyWeatherAPI（webAPI项目目录）\bin\Debug\net9.0\data\agents\01fcc3e5-0af7-49e6-ad7a-a760bd12dc4d
到
\MyWeatherAPI（解决方案目录）\MyWeatherAPI （webAPI项目目录）\data\agents 目录下
同时拷贝到 \MyWeatherAPI\MyWeatherPlugin\data\agents 目录下
以上步骤可能不是必须的，但是这么做不出错。（注意后面改了之后也同步拷贝下）

6）选择修改 MyWeatherPlugin\data\agents\01fcc3e5-0af7-49e6-ad7a-a760bd12dc4d下的配置信息
结构如图：
在这里插入图片描述
改动信息如下：
functions/get_my_weather_type.json

{"name": "get_my_weather_type","description": "获取所有的天气类型，您可以选择后返回响应的描述","parameters": {"type": "object","properties": {},"required": []}
}

functions/get_weather_des.json

{"name": "get_weather_des","description": "用户选择了天气后，获取关于天气的描述","parameters": {"type": "object","properties": {"weather_type": {"type": "string","description": "The type of the weather."}},"required": [ "weather_type" ]}
}

functions/get_weather_tool.json

{"name": "get_weather_tool","description": "提供一个适合天气出行的外带工具.","parameters": {"type": "object","properties": {"weather_type": {"type": "string","description": "天气类型."},"weather_desc": {"type": "string","description": "天气描述."}},"required": [ "weather_type", "weather_desc" ]}
}

instructions/instructions.liquid

你是一个关于天气资讯的小助手，你可以根据用户的天气问题，回复天气的描述和不同的天气要带什么工具出门。根据一下步骤进行操作:
1: 首先回答下用户今天是 {{current_date}},时间是 {{current_time}}.
2: 如果用户一些需求，你需要询问用户是否需要相关帮助。
3: 如果的问题中包含了关键词 {{weather_type}}，你就执行函数 get_my_weather_type 。

response/func.get_weather_des.0.liquid

{% assign weather_type = weather_type | downcase %}
{% if weather_type contains "晴天" -%}关于 {{weather_type}} 的描述是 is ${{ sunny_desc }}.您觉得满意吗?
{%- elsif weather_type contains "雨天" -%}关于 {{weather_type}} 的描述是 is ${{ rainny_desc }}.您觉得满意吗??
{%- elsif weather_type contains "雪天" -%}关于 {{weather_type}} 的描述是 is ${{ snowny_desc }}.您觉得满意吗??
{%- else -%}我们没有 {{weather_type}} 您天气的描述
{%- endif %}
{% if quantity == nil -%}How many slices would you like to order?
{%- endif %}

agent.json ---- 这个最重要，定义了需要大模型回调的函数声明

{"id": "01fcc3e5-0af7-49e6-ad7a-a760bd12dc4d","name": "Weather About","description": "weather.Talk","createdDateTime": "2024-05-07T10:00:00Z","updatedDateTime": "2024-05-07T10:00:00Z","disabled": false,"isPublic": true,"llmConfig": {"provider": "deepseek-ai","model": "deepseek-chat"},"profiles": [ "weather" ],"functions": [{"name": "get_my_weather_type","description": "获取所有的天气类型，您可以选择后返回响应的描述","parameters": {"type": "object","properties": {},"required": []}},{"name": "get_weather_des","description": "用户选择了天气后，获取关于天气的描述","parameters": {"type": "object","properties": {"weather_type": {"type": "string","description": "The type of the weather."}},"required": [ "weather_type" ]}},{"name": "get_weather_tool","description": "提供一个适合天气出行的外带工具.","parameters": {"type": "object","properties": {"weather_type": {"type": "string","description": "天气类型."},"weather_desc": {"type": "string","description": "天气描述."}},"required": [ "weather_type", "weather_desc" ]}}],"labels": [ "about" ]
}

其他基本不再重要了。

三、运行程序进行测试

刚刚 postman已经执行了 createagent 操作了。
继续执行接下来的 New Conversation 和 sendMessage 测试
在这里插入图片描述
执行结果如图：

四、总结

1、实现了通过 agent 控制调用不同的大模型。
2、通过 conversation 控制不同的会话上下文，相当于session，
3、通过输入内容，控制了让大模型来调用本地的函数，输出准确的结果。