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Unity AI-使用Ollama本地大语言模型运行框架运行本地Deepseek等模型实现聊天对话（二）

news 来源：原创 2025/4/29 6:42:36

一、使用介绍

官方网页：Ollama官方网址

中文文档参考：Ollama中文文档

二、运行示例

三、使用步骤

1、创建Canvas面板

具体层级如下

主要组件：发送按钮、输入框、滚动框

2、编写代码Webrequest

using System;
using System.Collections;
using System.Text;
using UnityEngine;
using UnityEngine.Networking;
using UnityEngine.UI;
using Button = UnityEngine.UI.Button;public class Webrequest : MonoBehaviour
{//curl http://localhost:11434/api/generate -H "Content-Type: application/json" -d "{ \"model\": \"llama3\", \"prompt\": \"你好\", \"stream\": false }"public Text text;public InputField input;public Button sendBtn;public ScrollRect scrollRect;void Start(){sendBtn.onClick.AddListener(OnSend);}private void Update(){if (Input.GetKeyDown(KeyCode.KeypadEnter) || Input.GetKeyDown(KeyCode.Return)){OnSend();}scrollRect.content.sizeDelta = text.rectTransform.sizeDelta;}void OnSend(){if (input.text != ""){text.text += $"你：{input.text}\n\n";scrollRect.verticalScrollbar.value = -0.1f;StartCoroutine(SendOllamaRequest(input.text));input.text = "";}else{text.text += "不能为空\n\n";scrollRect.verticalScrollbar.value = -0.1f;}}IEnumerator SendOllamaRequest(string value){// 目标 URLstring url = "http://localhost:11434/api/generate";string jsonData = $@"{{""model"": ""llama3.2"",""prompt"": ""{value}"",""stream"": false}}";// 创建 POST 请求UnityWebRequest request = new UnityWebRequest(url, "POST");byte[] bodyRaw = Encoding.UTF8.GetBytes(jsonData);request.uploadHandler = new UploadHandlerRaw(bodyRaw);request.downloadHandler = new DownloadHandlerBuffer();// 设置请求头request.SetRequestHeader("Content-Type", "application/json"); yield return request.SendWebRequest();// 处理响应if (request.result != UnityWebRequest.Result.Success){Debug.LogError($"Error: {request.error}");}else{string responseJson = request.downloadHandler.text;Debug.Log("Response: " + responseJson);// 解析 JSON 响应（示例）OllamaResponse response = JsonUtility.FromJson<OllamaResponse>(responseJson);// 访问字段Debug.Log($"模型: {response.model}");Debug.Log($"回复: {response.response}");text.text += "智能体：" + response.response + "\n\n";Debug.Log($"生成耗时: {response.eval_duration / 1e12} 秒");scrollRect.verticalNormalizedPosition = -0.1f;}}
}