demo
liu
11 months ago
parent
6c03205fde
commit
54cff96d45
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package com.supervision.nxllm.controller;
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import cn.hutool.core.io.FileUtil;
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import cn.hutool.core.map.MapUtil;
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import cn.hutool.core.util.StrUtil;
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import com.supervision.springaidemo.domain.ModelMetric;
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import com.supervision.springaidemo.dto.QARecordNodeDTO;
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import com.supervision.springaidemo.service.ModelMetricService;
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import com.supervision.springaidemo.service.NoteCheckRecordService;
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import com.supervision.springaidemo.thread.RunCheckThread;
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import com.supervision.springaidemo.thread.RunCheckThreadPool;
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import com.supervision.springaidemo.util.RecordRegexUtil;
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import com.supervision.springaidemo.util.WordReadUtil;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.ai.chat.messages.AssistantMessage;
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import org.springframework.ai.chat.messages.Message;
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import org.springframework.ai.chat.messages.SystemMessage;
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import org.springframework.ai.chat.messages.UserMessage;
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import org.springframework.ai.chat.prompt.Prompt;
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import org.springframework.ai.ollama.OllamaChatClient;
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import org.springframework.beans.factory.annotation.Autowired;
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import org.springframework.web.bind.annotation.GetMapping;
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import org.springframework.web.bind.annotation.RequestMapping;
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import org.springframework.web.bind.annotation.RestController;
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import java.io.File;
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import java.util.ArrayList;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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/**
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* 目前这是效果最好的方案,暂时先用这个
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*/
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@RestController
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@Slf4j
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@RequestMapping("exampleChat")
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public class ExampleChatController {
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private final OllamaChatClient chatClient;
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@Autowired
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private ModelMetricService modelMetricService;
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@Autowired
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private NoteCheckRecordService noteCheckRecordService;
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@Autowired
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public ExampleChatController(OllamaChatClient chatClient) {
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this.chatClient = chatClient;
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}
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private static final String exampleTemplate = """
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我们现在需要以step by step的方式进行笔录的指标分析工作,得到最终的结果并返回。
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step1:分析笔录的内容;
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以下是笔录的内容:
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---
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{context}
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---
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step2:现在给你指标以及指标的释义或例子可协助你参考:
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指标释义或例子及判断标准:
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指标名称:{metricName}
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我现在给你为true的例子可供参考:{example};
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如果和例子这种情况相反或偏差较大则为false;
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如果笔录中,没有任何笔录内容涉及到该项指标,无法进行判断,则为empty。
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step3:现在需要你根据上面提供的所有信息,尽可能实事求是完成判断:
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1.判断结论:true/false/empty
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2.得到结论的笔录原话:从笔录的对话中,得到该结论的原文(一定是摘抄的原文且为中文)。如果结论为true,则必须要有原文佐证!
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3.得到结论的原因:分析得出该结论的原因,需明确说明为什么得到该结论,需要参考例子的格式且为中文回复。如果结论为true/false,则必须有原因!
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step4:必须以json格式回复, JSON的value内容我给你的提示,在实际输出的时候不需要带上:
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---
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{"result":"结论", "originalContext":"笔录对应原话","reason":"原因"}
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---
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好了,现在可以回复了!
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""";
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@GetMapping("exampleChat")
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public void exampleChat() {
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File file = FileUtil.file("/Users/flevance/Desktop/宁夏审讯大模型/裴金禄/行为人和受害人/裴金禄第一次.docx");
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String context = WordReadUtil.readWord(file.getPath());
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List<QARecordNodeDTO> qaList = RecordRegexUtil.recordRegex(context, "裴金禄");
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for (QARecordNodeDTO qaRecordNodeDTO : qaList) {
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// 只查入罪指标
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List<ModelMetric> list = modelMetricService.lambdaQuery().likeRight(ModelMetric::getMetricCode, "RZ").list();
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for (ModelMetric modelMetric : list) {
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String systemPrompt = """
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你是一个善于分析办案笔录的模型,能够根据办案笔录的回答内容,结合给定的例子,实事求是的判断给定指标是否满足。注意,仅根据笔录进行分析,不要做笔录之外的推断。笔录内容可能比较长,可能分多次提交给你。Think step by step
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""";
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List<Message> messages = new ArrayList<>(List.of(new SystemMessage(systemPrompt)));
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Map<String, Object> param = new HashMap<>();
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param.put("context", qaRecordNodeDTO.toString());
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param.put("metricName", modelMetric.getMetricName());
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param.put("example", StrUtil.format(modelMetric.getExample(), MapUtil.of("action", "裴金禄")));
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String format = StrUtil.format(exampleTemplate, param);
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List<Message> userMessageList = new ArrayList<>();
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if (format.length() > 8000) {
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log.info("分段提交");
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for (String s : StrUtil.split(format, 6000)) {
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userMessageList.add(new UserMessage(s));
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userMessageList.add(new AssistantMessage("继续"));
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}
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userMessageList.remove(userMessageList.size() - 1);
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} else {
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userMessageList.add(new UserMessage(format));
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}
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messages.addAll(userMessageList);
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RunCheckThread runCheck = new RunCheckThread("裴金禄尝试正则来做", chatClient, noteCheckRecordService, new Prompt(messages), FileUtil.getName(file), format, systemPrompt, modelMetric, 0);
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RunCheckThreadPool.chatExecutor.submit(runCheck);
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}
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}
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}
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}
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