本篇咱们来聊一聊怎么在 .NET 代码中使用本地部署的 Deepseek 语言模型。大伙伴们不必要紧张，很简单的，你不需要学习新知识，只要你知道 .NET 如何访问 HTTP 和 JSON 的序列化相关就够了。
先说说如何弄本地模型，有伙伴会问：直接用在线的不好？其实，本地部署更实用，也更符合安全要求。其实，AI 真正用于生产环境反而不需要那么“强大”，能有行业化定制模型会更好，这样在专业领域的预测算法更准确；只有用于娱乐产业才需要“面面俱到”。
网上关于本地化部署的教程太多了，所以老周只是简单描述一下。而且，老周也相信大伙伴们的能力，连代码都会写了，怎么会搞不定这些活儿呢？
1、安装 Ollama，官网：Ollama。各位注意，这年头下载软件一定要看清楚是不是官网，现在国内很多冒充官网骗钱的，所以，看好域名很重要（比如，OBS、VS Code 等也有很多假域名）。Ollama 就是专门用于调用 LLM 的工具，它会启动本地服务器（Web），通过 Web API 方式交互。该工具是跨平台的，使用 C 和 Go 语言开发，性能上不用担心，它不是用某脚本语言开发的。这个下载后直接安装就完事了，无难度。
2、先不急着下载模型，看看你的 C 盘空间够不够下载模型，不够的话，请配置一下 OLLAMA_MODELS 环境变量，指向你要存放模型的目录。这个都会配置了，不用老周说了吧，基于用户和基于机器的环境变量都可以。
Ollama 默认启动 Web 服务器的本地地址是 http://127.0.0.1:11434，如果端口有冲突，可以用 OLLAMA_HOST 环境变量自己配置一下。没其他要求，就按默认就行了，不用配置。配置时要写上完整的 HTTP 地址，如 http://192.168.1.15:8819。这个你看看源代码就知道为什么要写完整 URL 了。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | func Host() *url.URL { defaultPort := "11434" s := strings.TrimSpace(Var("OLLAMA_HOST")) scheme, hostport, ok := strings.Cut(s, "://") switch { case !ok: scheme, hostport = "http", s case scheme == "http": defaultPort = "80" case scheme == "https": defaultPort = "443" } hostport, path, _ := strings.Cut(hostport, "/") host, port, err := net.SplitHostPort(hostport) if err != nil { host, port = "127.0.0.1", defaultPort if ip := net.ParseIP(strings.Trim(hostport, "[]")); ip != nil { host = ip.String() } else if hostport != "" { host = hostport } } if n, err := strconv.ParseInt(port, 10, 32); err != nil || n > 65535 || n |
3、第一次启动大语言模型需要下载，在 Ollama 官网进入“Models”页面，你基本不用找了，现在最热门的就是它了。
点击模型链接，进入详细页。下拉列表能看到模型大小，视你的 CPU 或 GPU 配置来选吧。
老周的机器 CPU 是 i5-11400F，跑 8B 问题不大（显卡是 4G 显存），回答问题一般要二三十秒，能接受。如果你不确定，可以从 7B 开始测试。页面向下滚动会告诉你命令行怎么用，比如，要下载7B的模型，执行命令 ollama run deepseek-r1:7b。
模型名称后面有个冒号，之后是模型的大小。执行后就是坐和等待。下载时间取决于网速和运气。
4、下载完后，你就可以问 Deepseek 问题了，输入问题，回车就行了。你关闭了控制台，手动启动的方法就是上面下载模型的命令（只想下载，不运行，可以将 run 替换为 pull），ollama run XXXX:7B，但这次它不会再下载了，而是直接启动。
好了，准备工作结束，下面正片开始。
和调用一般 Web API 一样，HTTP 协议，JSON 格式。要和模型交互，会用到两个API。
1、单次对话（你问，它回答，类似搜索，这种较常用）。
1 | POST /api/generate |
要提交的JSON一般只需要三个参数（其他你可以看文档，其实有些参数老周也没看懂）：
model：LLM 模型的名称，毕竟 Ollama 可以下载多种模型，所以要指定模型，如 deepseek-r1:7b；
prompt：你要问它的问题，比如“何不食肉糜？”；
stream：是否启用流式传输。如果是 false，你发出请求后，要等到所有回答内容生成后，一次性返回。如果是 true，可以分块返回，不必等到全部生成你就可以读了。
返回的 JSON 对象中，response 字段就是 LLM 回答你的内容，如果是流式返回，最后一段回复的 done 字段会为 true，其他片段为 false。
2、聊天模式
1 | POST /api/chat |
请求的 JSON 常用的字段和上面单次对话一样，但 prompt 字段换成 messages 字段。此字段是数组类型，包含多个对象，代表聊天记录。其中，role 代表角色，你是 user，AI是 assistant。content 代表聊天消息内容。在调用时，可以把前面的聊天记录放进 messages 数组。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | { "model": "deepseek-r1:32b", "stream": false, "messages": [ { "role": "user", "content": "你好啊" }, { "role": "assistant", "content": "我不好" }, { "role": "user", "content": "为什么不好？" } ]} |
返回的 JOSN 对象中，message 字段就是新的聊天记录（一般是 AI 回复你的）。
好了，知识就介绍到这儿，下面咱们要实际操作了。先声明一下：Ollama API 的调用是有专门的 Nuget 包的，都封装好的，你不需要自己干活。不过，为了让大伙伴们好理解，老周下面的演示暂不使用 Nuget 包。
先来个简单的，关闭流模式。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | internal class Program { // 请求URL // API路径 const string API_GEN = "/api/generate"; static async Task Main(string[] args) { using HttpClient client = new(); // 设置基址 client.BaseAddress = new Uri(BS_URL); // 请求数据 string senddata = """ { "model": "deepseek-r1:8b", "prompt": "黄河有多长？", "stream": false } """; StringContent content = new(senddata, Encoding.UTF8, new MediaTypeHeaderValue("application/json")); HttpRequestMessage msg = new HttpRequestMessage() { Method = HttpMethod.Post, Content = content }; // 设置相对的URL即可 msg.RequestUri = new Uri(API_GEN, UriKind.Relative); // 发送请求，并读取响应消息 HttpResponseMessage respmsg = await client.SendAsync(msg); if (respmsg.IsSuccessStatusCode) { string json_back = await respmsg.Content.ReadAsStringAsync(); Console.WriteLine("-------------- 返回的JSON ----------------n"); Console.WriteLine(json_back); } } } |
代码运行后，需要等待一段时间。请求返回后，将得到以下 JSON：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | { "model": "deepseek-r1:8b", "created_at": "2025-02-23T05:12:46.9156624Z", "response": "u003cthinku003en嗯，用户问“黄河有多长？”首先，我得回想一下黄河的基本信息。黄河是中国的一条重要河流，发源于青藏高原，流经华北平原，注入渤海湾。nn接下来， 我需要确定黄河的长度。我记得它被称为“九曲黄河”，这可能是因为它有很多弯曲的河道。根据我所知道的资料，黄河的总长大约在5463公里左右。不过，这个数字好像有点问题，因为有些资料会提到水系更长的情况，比如包括支流在内。nn另外，我还需要考虑用户为什么会问这个问题。也许他们是在做研究、写作业，或者只是出于好奇。我应该确认一下数据的准确性，避免误导用户。如果有时间的话，最好核实一下最新的资料，但目前我只能依据已有的信息回答。nn在解释时，我会提到黄河的重要性，比如它对中国历史、文化的影响，以及作为水资源的重要性。这不仅能满足用户的基本问题，还能提供更多背景信息，让回答更丰富。nn最后，考虑到用户可能需要进一步了解，可以建议他们查阅最新的地理资料或相关文献，以确保得到准确的数据和详细的信息。nu003c/thinku003enn黄河的长度约为5,463公里（3,455英里），它是中国最长的河流之一，从青藏高原的玛旁雍措开始，经过九曲，最终注入渤海湾。", "done": true, "done_reason": "stop", "context": [...], "total_duration": 46687936200, "load_duration": 6910762400, "prompt_eval_count": 9, "prompt_eval_duration": 1643000000, "eval_count": 347, "eval_duration": 38128000000} |
其中，response 字段就是模型所回答的内容。done: true 表示所有回复已完成。
当流模式关闭时，响应消息是一次性返回的。下面咱们开启流模式，看会发生什么。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | internal class Program { // 请求URL // API路径 const string API_GEN = "/api/generate"; static async Task Main(string[] args) { using HttpClient client = new(); // 设置基址 client.BaseAddress = new Uri(BS_URL); // 请求数据 string senddata = """ { "model": "deepseek-r1:8b", "prompt": "人为什么不能有两个头？", <strong>"stream": true</strong> } """; StringContent content = new(senddata, Encoding.UTF8, new MediaTypeHeaderValue("application/json")); HttpRequestMessage msg = new HttpRequestMessage() { Method = HttpMethod.Post, Content = content }; // 设置相对的URL即可 msg.RequestUri = new Uri(API_GEN, UriKind.Relative); // 发送请求，并读取响应消息 HttpResponseMessage respmsg = await client.SendAsync(msg, <strong>HttpCompletionOption.ResponseHeadersRead</strong>); if (respmsg.IsSuccessStatusCode) { <strong>StreamReader reader </strong><strong>= new(await respmsg.Content.ReadAsStreamAsync());</strong> string? line; while( (line = <strong>reader.ReadLine()</strong>) != null ) { Console.WriteLine(line); } } } } |
使用流模式后，有几个地方要改：
1、POST 的内容中，stream 字段要设置为 true；
2、调用 HttpClient.SendAsync 方法时，要指定 HttpCompletionOption 枚举值 ResponseHeadersRead，它表示：客户端不需要等到所有响应都完成，只要读到 Header 就可以返回；
3、以流的方式读取，所以为了方便一行一行地读，需要创建一个 StreamReader 实例。因为分区返回的 JSON 文本之间会有换行符，所以，咱们可以一行一行地读。
运行后你会发现，响应消息是几个字几个字地返回，这样模拟它的思考过程，即返回多个 JSON 对象。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 | {"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:26.8283737Z","response":"u003cthinku003e","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:26.935257Z","response":"n","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.0400684Z","response":"嗯","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.1481036Z","response":"，","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.2553207Z","response":"为什么","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.361141Z","response":"人类","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.4645525Z","response":"不能","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.5709536Z","response":"像","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.678601Z","response":"一些","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.7789896Z","response":"科","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.8843042Z","response":"幻","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:27.9865743Z","response":"作品","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.0981338Z","response":"中","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.2029637Z","response":"那样","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.3082135Z","response":"拥有","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.409586Z","response":"两个","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.51381Z","response":"头","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.6184175Z","response":"呢","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.7219117Z","response":"？","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.825078Z","response":"这","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:28.9296468Z","response":"听","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.0311444Z","response":"起来","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.1316079Z","response":"似乎","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.2358219Z","response":"是不","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.3398343Z","response":"可能","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.4403529Z","response":"的","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.5484937Z","response":"，但","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.655022Z","response":"我","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.7581549Z","response":"想","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.8581566Z","response":"深","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:29.9648745Z","response":"入","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.0696775Z","response":"探","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.1753713Z","response":"讨","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.27877Z","response":"一下","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.3833475Z","response":"这个","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.4894236Z","response":"问题","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.5940864Z","response":"。","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.6990377Z","response":"首","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.8045853Z","response":"先","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:30.9119286Z","response":"，我","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.0145369Z","response":"需要","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.1199967Z","response":"了解","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.2210625Z","response":"一下","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.3265552Z","response":"人的","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.4297025Z","response":"身体","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.5367356Z","response":"结构","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.6424456Z","response":"。","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.7482556Z","response":"我们","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.8530019Z","response":"知道","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:31.9602229Z","response":"，","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:32.0648399Z","response":"人","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:32.1710681Z","response":"体","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:32.2750219Z","response":"是","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:32.3830126Z","response":"由","done":false}{"model":"deepseek-r1:8b","created_at":"2025-02-23T08:52:32.4867969Z","response":" 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最后一个 JSON 对象的 done 字段为 true，表示是最后一个消息分块。context 字段中的数字是用于对话上下文的，即下一次你向 LLM 发问时，可以把上次返回的 context 放到请求数据中，这样形成基于上下文的推理。不过这个 context 字段在官方文档中已标记为“过时”，以后可能不使用了。所以咱们可以不理会，因为可以使用聊天模式 API（请看上文）。
通过上面的简单演示，相信大伙伴都会用了。不过为了方便，咱们可以把请求数据封装一下，通过 JSON 序列化来调用，会更方便。
A、请求消息，主要使用 model、prompt 和 stream 字段。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | /// <summary> /// 请求数据 /// </summary> public class ModelRequest { /// <summary> /// 模型名称 /// </summary> public string? Model { get; set; } /// <summary> /// 提问 /// </summary> public string? Prompt { get; set; } /// <summary> /// 是否使用流模式 /// </summary> public bool Stream { get; set; } } |
B、返回的JSON封装，用到 model、response、done 字段。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | /// <summary> /// 返回的消息 /// </summary> public class ModelResponse { /// <summary> /// 模型名 /// </summary> public string? Model { get; set; } /// <summary> /// 回复内容 /// </summary> public string? Response { get; set; } /// <summary> /// 是否为最后一个分块 /// </summary> public bool Done { get; set; } = false; } |
咱们测试一下流模式。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | internal class Program { // 根URL // API 路径 const string GEN_API = "/api/generate"; static async Task Main(string[] args) { using HttpClient client = new HttpClient(); // 设置基址 client.BaseAddress = new Uri(BASE_URL); // 准备请求数据 ModelRequest rqdata = new() { Model = "deepseek-r1:8b", Stream = true, Prompt = "25的15次方是多少？" }; // 发送请求 var reqmsg = new HttpRequestMessage(HttpMethod.Post, GEN_API); // 请求正文 JsonContent data = JsonContent.Create(rqdata); reqmsg.Content = data; var responsemsg = await client.SendAsync(reqmsg, HttpCompletionOption.ResponseHeadersRead); // 处理响应消息 if (responsemsg.IsSuccessStatusCode) { using StreamReader reader = new(await responsemsg.Content.ReadAsStreamAsync()); // 属性名不区分大小写，在反序列化时如果不启用该选项 // 将无法读到字段值 JsonSerializerOptions options = new() { <strong>PropertyNameCaseInsensitive = true</strong> }; var line = await reader.ReadLineAsync(); while (line != null) { ModelResponse? mdresp = JsonSerializer.Deserialize(line, options); if(mdresp != null) { Console.Write(mdresp.Response); // 是否为最后一块 if(mdresp.Done == true) { Console.Write("nn（已完毕）n"); break; } } line = await reader.ReadLineAsync(); } } } } |
这个和上一个例子差不多，只是将数据封装了。
需要注意的是，由于流是不断地返回 JSON 对象，而不是一个单独的JSON数组，所以不应该直接返序列化为 ModelResponse 数组，而是和前面一样，读一行出来，用 JsonSerializer.Deserialize 方法进行反序列化。这里要用到 JsonSerializerOptions 类设置一个 PropertyNameCaseInsensitive 属性，这是因为返回的 JSON 的字段名全是小写的，而咱们定义的 ModelResponse 类的属性是大写字母开头的，默认处理是严格区分大小写的（反序列化的时候，序列化时可以忽略），设置该选项是让其能够赋值。如果你嫌麻烦，也可以把 ModelResponse 类的属性名称全定义为小写。
使用流模式调用，可以得到这样的效果：
不过，不得不说一句，AI 做数学运算不如直接让 CPU 进行运算，秒出结果。这厮一直在瞎推理，推来推去，总算得出答案。所以说，AI 是有其适用范围的，真不是任何场合都合适。
下面咱们使用一下 nuget 包，搜索 OllamaSharp 就能找到。
如何添加 nuget 包，此处省略 1851 字。
安装好包后，需要引入以下命名空间：
1 2 | using OllamaSharp; using OllamaSharp.Models; |
然后，实例化 OllamaApiClient。
1 2 3 4 |
构造函数的第二个参数是设置一个默认模型名称，后面在调用 API 时就不用再设置了。
请求时要提交一个 GenerateRequest 对象，和前面咱们自己封装的一样，需要用到 Model、Prompt 等属性。
1 2 3 4 5 6 | // 准备数据GenerateRequest req = new();// 如果在构造函数配置过模型名称，可以省略//req.Model = "deepseek-r1:8b";req.Stream = true;req.Prompt = "唐太宗是唐朝第几位皇帝？"; |
由于咱们设置了默认模型，这里 Model 属性可以不赋值。
发出请求，返回一个 IAsyncEnumerable 对象，里面包含返回的 GenerateResponseStream 对象列表。这个支持异步枚举。
1 2 3 4 5 6 7 8 9 10 11 12 13 | var resp = client.GenerateAsync(req);await foreach(GenerateResponseStream? stream in resp){ if(stream != null) { Console.Write(stream.Response); // 如果已结束 if(stream.Done) { Console.Write("nn会话结束n"); } }} |
其实和咱们刚才自己封装的差不多。运行结果如下：
虽然答案是对的，但推理过程纯属胡说，大伙伴且当娱乐。
下面咱们用一下聊天功能。
这是一个 WinForm 项目，窗口上方是一个 RichTextBox，显示AI回复的内容，下面的文本框用来输入。
往项目添加 OllamaSharp 包，然后在窗口类的代码文件中引入以下命名空间：
1 2 | using OllamaSharp;using OllamaSharp.Models.Chat; |
聊天模式也是先创建 OllamaApiClient 实例，然后把此 OllamaApiClient 实例传递给 Chat 类的构造函数，进而创建 Chat 实例。接着，调用 Chat 实例的 SendAsAsync 或 SendAsync 方法发送消息。方法返回 AI 回答你的内容。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | // 声明类级别的私有字段 OllamaApiClient? client; Chat? theChat; public Form1() { InitializeComponent(); // 调用这个方法初始化 Ollama 客户端 InitOllamClient(); btnSend.Click += BtnSend_Click; } private void InitOllamClient() { client = <strong>new OllamaApiClient("http://127.0.0.1:11434", "deepseek-r1:8b"</strong><strong>)</strong>; theChat = <strong>new</strong><strong> Chat(client)</strong>; } |
处理一下“发送”按钮的 Click 事件，发送消息并把AI响应的消息追加到 RichTextBox 中。
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | private async void BtnSend_Click(object? sender, EventArgs e) { if (txtMessage.Text == string.Empty) return; // 发起请求 IAsyncEnumerable history = <strong>theChat!</strong><strong>.SendAsAsync(ChatRole.User, txtMessage.Text)</strong>; await foreach(string s in history) { // 跳过不需要的字符 if(s.Equals("") || s.Equals("") || s.Equals("nn") || s.Equals("n")) { continue; } // 追加文本 <strong>rtbMessages.AppendText(s)</strong>; } // 一条消息后加一个换行符 rtbMessages.AppendText("n"); txtMessage.Clear(); // 清除文本 } |
SendAsAsync 方法有N多重载，此处使用的是以下版本：
1 | public IAsyncEnumerable SendAsAsync(ChatRole role, string message, CancellationToken cancellationToken = default(CancellationToken)) |
第一个参数是 role 表示角色，你说的话要用 User 角色；第二个参数是消息内容；第三个可选，一般默认就行，除非你需要取消调用。
返回的内容是字符串列表，这个列表包含模型预测的短语列表，要把整个列表串联起来才是完整的消息（看前文第一个例子，就是流模式那样）。
效果如下图所示：
好了，本文内容基本介绍完了。老周可不允许你学不会！如果这也学不会，那就罚自己饿两个月吧。ASP.NET Core 上的用法也一样的，你可以让 Ollama 和应用程序在同一个服务器，本地调用，然后返回给客户端，一般不用直接对外暴露 URL。
以上就是详解如何在.NET代码中使用本地部署的Deepseek语言模型的详细内容，更多关于.NET使用本地部署Deepseek的资料请关注IT俱乐部其它相关文章！
