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@ -11,11 +11,12 @@ import cc.hiver.mall.purchaseocr.vo.PurchaseOcrExample; |
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import cc.hiver.mall.service.mybatis.*; |
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import cn.hutool.core.date.StopWatch; |
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import com.alibaba.dashscope.aigc.generation.Generation; |
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import com.alibaba.dashscope.aigc.generation.GenerationParam; |
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import com.alibaba.dashscope.aigc.generation.GenerationResult; |
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import com.alibaba.dashscope.aigc.generation.models.QwenParam; |
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import com.alibaba.dashscope.aigc.multimodalconversation.*; |
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import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation; |
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import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam; |
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import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult; |
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import com.alibaba.dashscope.common.Message; |
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import com.alibaba.dashscope.common.MessageManager; |
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import com.alibaba.dashscope.common.MultiModalMessage; |
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import com.alibaba.dashscope.common.Role; |
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import com.alibaba.dashscope.exception.ApiException; |
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@ -50,6 +51,61 @@ public class AliOcrUtil { |
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private static final Pattern COMPILE = Pattern.compile("\\D+"); |
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private static final Pattern SYYS = Pattern.compile("所有颜色", Pattern.LITERAL); |
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private static final Pattern SYCM = Pattern.compile("所有尺码", Pattern.LITERAL); |
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/** 图像识别：效果优先 */ |
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private static final String MODEL_VISION = "qwen3.5-plus"; |
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/** 语音/文本开单：速度优先（纯文本走 Generation，通常可压到 1s 左右） */ |
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private static final String MODEL_TEXT = "qwen-flash"; |
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private static Map<String, Object> mmText(String text) { |
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final Map<String, Object> content = new HashMap<>(); |
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content.put("text", text); |
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return content; |
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} |
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private static Map<String, Object> mmImage(String image) { |
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final Map<String, Object> content = new HashMap<>(); |
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content.put("image", image); |
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return content; |
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} |
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private static MultiModalMessage mmMessage(String role, List<Map<String, Object>> content) { |
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return MultiModalMessage.builder().role(role).content(content).build(); |
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} |
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private static String extractMultiModalText(MultiModalConversationResult result) { |
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final List<Map<String, Object>> content = result.getOutput().getChoices().get(0).getMessage().getContent(); |
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if (content == null || content.isEmpty() || content.get(0).get("text") == null) { |
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return ""; |
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} |
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return content.get(0).get("text").toString(); |
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} |
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/** |
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* 文本开单走 Generation + 轻量模型，比 qwen3.5-plus 多模态快很多。 |
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*/ |
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private static String callTextFast(String systemPrompt, String userPrompt) |
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throws ApiException, NoApiKeyException, InputRequiredException { |
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final Generation gen = new Generation(); |
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final List<Message> messages = new ArrayList<>(); |
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messages.add(Message.builder().role(Role.SYSTEM.getValue()).content(systemPrompt).build()); |
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messages.add(Message.builder().role(Role.USER.getValue()).content(userPrompt).build()); |
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final GenerationParam param = GenerationParam.builder() |
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.model(MODEL_TEXT) |
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.messages(messages) |
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.resultFormat(GenerationParam.ResultFormat.MESSAGE) |
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.temperature(0.2F) |
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.topP(0.8) |
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.maxTokens(1024) |
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.enableThinking(false) |
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.build(); |
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log.info("DashScope 文本调用开始, model={}", MODEL_TEXT); |
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final long start = System.currentTimeMillis(); |
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final GenerationResult result = gen.call(param); |
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final String text = result.getOutput().getChoices().get(0).getMessage().getContent(); |
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log.info("DashScope 文本调用结束, cost={}ms, textLength={}", System.currentTimeMillis() - start, |
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text == null ? 0 : text.length()); |
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return text == null ? "" : text; |
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} |
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/** |
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* AI入库图像识别-多轮对话 |
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@ -83,11 +139,9 @@ public class AliOcrUtil { |
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Constants.apiKey = "sk-bcfa4865b89548acb8225f910f13d682"; |
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final CopyOnWriteArrayList<MultiModalConversationResult> multiModalConversationResults = new CopyOnWriteArrayList<>(); |
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final MultiModalConversation conv = new MultiModalConversation(); |
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final MultiModalMessageItemText systemText = new MultiModalMessageItemText("You are a helpful assistant."); |
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final MultiModalConversationMessage systemMessage = MultiModalConversationMessage.builder() |
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.role(Role.SYSTEM.getValue()).content(Collections.singletonList(systemText)).build(); |
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final MultiModalMessage systemMessage = mmMessage(Role.SYSTEM.getValue(), |
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Collections.singletonList(mmText("You are a helpful assistant."))); |
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final MultiModalMessageItemImage userImage = new MultiModalMessageItemImage(picturePath); |
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final String firstQuestionMsg = "请把图片中的内容按照商品的不同规格拆分，返回" + count + "条JSON数据。\n" + |
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" JSON示例:[{ \"productSn\": \"" + productSnExample + "\", \"productName\":\"" + productNameExample + "\", \"attributeList\": \"{'color':'" + purchaseOcrExample.getColor() + "','size':'" + purchaseOcrExample.getSize() + "'}\", \"productCount\": \"" + purchaseOcrExample.getProductCount() + "\", \"price\": \"" + purchaseOcrExample.getPrice() + "\" }]。" + |
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"以下是几点要求： " + |
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@ -99,23 +153,23 @@ public class AliOcrUtil { |
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"6.请注意返回JSON符号解析格式正确 ,确保java程序能够正确解析。" + |
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"7.严格返回" + count + "条JSON数据，如果数量有偏差按照实际条数返回，不能自动省略！"; |
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log.info("AI提问内容：" + firstQuestionMsg); |
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MultiModalMessageItemText userText = new MultiModalMessageItemText(firstQuestionMsg); |
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final MultiModalConversationMessage userMessage = |
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MultiModalConversationMessage.builder().role(Role.USER.getValue()) |
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.content(Arrays.asList(userImage, userText)).build(); |
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final CopyOnWriteArrayList<MultiModalConversationMessage> messages = new CopyOnWriteArrayList<>(); |
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final MultiModalMessage userMessage = mmMessage(Role.USER.getValue(), |
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Arrays.asList(mmImage(picturePath), mmText(firstQuestionMsg))); |
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final CopyOnWriteArrayList<MultiModalMessage> messages = new CopyOnWriteArrayList<>(); |
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messages.add(systemMessage); |
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messages.add(userMessage); |
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final MultiModalConversationParam param = MultiModalConversationParam.builder() |
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.model("qwen-vl-max") |
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.model(MODEL_VISION) |
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.messages(messages) |
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.enableThinking(false) |
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.build(); |
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stopWatch.start("一轮会话"); |
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log.info("DashScope 图像识别开始, model={}, picture={}", MODEL_VISION, picturePath); |
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MultiModalConversationResult result = conv.call(param); |
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stopWatch.stop(); |
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multiModalConversationResults.add(result); |
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// 解析结果
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String text = result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text").toString(); |
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String text = extractMultiModalText(result); |
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log.info("一轮对话-的json======" + text); |
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// 根据{}截取数据
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final int startIndex = text.indexOf('{'); |
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@ -170,21 +224,15 @@ public class AliOcrUtil { |
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log.error(Thread.currentThread().getName() + "-一轮对话--->json识别报错喽，没关系，我来重新识别一下！"); |
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//尝试重新识别一次
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final String errorJsonQuestionMsg = "返回的json格式有误，请重新返回。"; |
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final MultiModalMessageItemText assistentText = new MultiModalMessageItemText( |
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text); |
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final MultiModalConversationMessage assistentMessage = MultiModalConversationMessage.builder() |
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.role(Role.ASSISTANT.getValue()).content(Collections.singletonList(assistentText)).build(); |
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messages.add(assistentMessage); |
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userText = new MultiModalMessageItemText(errorJsonQuestionMsg); |
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messages.add(MultiModalConversationMessage.builder().role(Role.USER.getValue()) |
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.content(Collections.singletonList(userText)).build()); |
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param.setMessages(new CopyOnWriteArrayList<Object>(messages)); |
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messages.add(mmMessage(Role.ASSISTANT.getValue(), Collections.singletonList(mmText(text)))); |
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messages.add(mmMessage(Role.USER.getValue(), Collections.singletonList(mmText(errorJsonQuestionMsg)))); |
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param.setMessages(new CopyOnWriteArrayList<>(messages)); |
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stopWatch.start("一轮对话json格式错误重新识别！"); |
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result = conv.call(param); |
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multiModalConversationResults.add(result); |
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stopWatch.stop(); |
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// 解析结果
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text = result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text").toString(); |
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text = extractMultiModalText(result); |
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log.info("一轮对话--json格式错误，重新识别的结果======" + text); |
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// 根据{}截取数据
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final int secondStartIndex = text.indexOf('{'); |
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@ -242,21 +290,15 @@ public class AliOcrUtil { |
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final int missingCount = count - fristResultCount; |
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if (missingCount > 0) { |
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final String secondQuestionMsg = "缺少了" + missingCount + "条，请完整返回。"; |
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final MultiModalMessageItemText assistentText = new MultiModalMessageItemText( |
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text); |
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final MultiModalConversationMessage assistentMessage = MultiModalConversationMessage.builder() |
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.role(Role.ASSISTANT.getValue()).content(Collections.singletonList(assistentText)).build(); |
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messages.add(assistentMessage); |
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userText = new MultiModalMessageItemText(secondQuestionMsg); |
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messages.add(MultiModalConversationMessage.builder().role(Role.USER.getValue()) |
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.content(Collections.singletonList(userText)).build()); |
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param.setMessages(new CopyOnWriteArrayList<Object>(messages)); |
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messages.add(mmMessage(Role.ASSISTANT.getValue(), Collections.singletonList(mmText(text)))); |
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messages.add(mmMessage(Role.USER.getValue(), Collections.singletonList(mmText(secondQuestionMsg)))); |
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param.setMessages(new CopyOnWriteArrayList<>(messages)); |
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stopWatch.start("二轮会话"); |
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result = conv.call(param); |
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multiModalConversationResults.add(result); |
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stopWatch.stop(); |
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// 解析结果
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String secondText = result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text").toString(); |
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String secondText = extractMultiModalText(result); |
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log.info("二轮对话识别结果：" + secondText); |
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// 根据{}截取数据
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final int secondStartIndex = secondText.indexOf('{'); |
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@ -322,21 +364,15 @@ public class AliOcrUtil { |
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log.error(Thread.currentThread().getName() + "-二轮对话--->json识别报错喽，没关系，我来重新识别一下！"); |
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//尝试重新识别一次
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final String errorJsonQuestionMsg = "返回的json格式有误，请重新返回。"; |
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final MultiModalMessageItemText secondJsonErrorAssistentText = new MultiModalMessageItemText( |
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secondText); |
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final MultiModalConversationMessage secondJsonErrorAssistentMessage = MultiModalConversationMessage.builder() |
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.role(Role.ASSISTANT.getValue()).content(Collections.singletonList(secondJsonErrorAssistentText)).build(); |
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messages.add(secondJsonErrorAssistentMessage); |
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userText = new MultiModalMessageItemText(errorJsonQuestionMsg); |
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messages.add(MultiModalConversationMessage.builder().role(Role.USER.getValue()) |
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.content(Collections.singletonList(userText)).build()); |
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param.setMessages(new CopyOnWriteArrayList<Object>(messages)); |
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messages.add(mmMessage(Role.ASSISTANT.getValue(), Collections.singletonList(mmText(secondText)))); |
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messages.add(mmMessage(Role.USER.getValue(), Collections.singletonList(mmText(errorJsonQuestionMsg)))); |
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param.setMessages(new CopyOnWriteArrayList<>(messages)); |
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stopWatch.start("二轮会话json格式错误，重新识别！"); |
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result = conv.call(param); |
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multiModalConversationResults.add(result); |
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stopWatch.stop(); |
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// 解析结果
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secondText = result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text").toString(); |
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secondText = extractMultiModalText(result); |
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log.info("二轮对话-json格式错误，重新识别的结果======" + secondText); |
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// 根据{}截取数据
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final int errorSsecondStartIndex = secondText.indexOf('{'); |
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@ -531,7 +567,7 @@ public class AliOcrUtil { |
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} |
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})).build(); |
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final MultiModalConversationParam param = MultiModalConversationParam.builder() |
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.model("qwen-vl-max") |
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.model(MODEL_VISION) |
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.message(userMessage) |
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/*.stream(true) |
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.temperature(1F) |
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@ -543,6 +579,7 @@ public class AliOcrUtil { |
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.seed(500)*/ |
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// 是否使用增量输出。当使用增量输出时每次流式返回的序列仅包含最新生成的增量内容，默认值为false，即输出完整的全量内容
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.incrementalOutput(true) |
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.enableThinking(false) |
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.build(); |
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stopWatch.start("开始识别！"); |
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final Flowable<MultiModalConversationResult> result = conv.streamCall(param); |
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@ -833,10 +870,6 @@ public class AliOcrUtil { |
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final JSONObject jsonObject = new JSONObject(); |
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Constants.apiKey = "sk-bcfa4865b89548acb8225f910f13d682"; |
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final StopWatch stopWatch = new StopWatch("Ai回答计时"); |
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final Generation gen = new Generation(); |
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final MessageManager msgManager = new MessageManager(10); |
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final Message systemMsg = |
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Message.builder().role(Role.SYSTEM.getValue()).content("You are a helpful assistant.").build(); |
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//questionMsg += "请帮我把所有内容封装为JSON，json格式为：[{ \"productSn\": \"货号\", \"attributeList\": [{\"color\":\"颜色\",\"size\":\"尺码\",\"productCount\": \"数量\"}] }]。以下是几点要求： 1.“货号”两个字和颜色中间的内容代表productSn，productSn可能包含\"新\"、\"退\"、\"旧\"、\"换\"、\"补\"。 2.\"SYYS色\"代表“所有颜色”，\"color\"字段返回“SYYS色”。 3.\"SYCM码\"代表“所有尺码”，\"size\"字段返回“SYCM码”。 4.只输出JSON数据即可，不用返回字段描述和解析过程。";
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questionMsg += "你是一个服装行业库管专家，请帮我把所有内容封装为JSON，json格式为：[{ \"productSn\": \"货号\", \"attributeList\": [{\"color\":\"颜色\",\"size\":\"尺码\",\"productCount\": \"数量\"}] , \"wholesalePrice\":\"价格\"}]，\n" + |
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"1.productSn的返回值中去掉颜色，尺码等信息，\n" + |
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@ -848,22 +881,9 @@ public class AliOcrUtil { |
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"7.如果没有识别到\"wholesalePrice\"的内容，则\"wholesalePrice\"赋值\"0\"。\n" + |
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"8.只输出JSON数据即可，禁止返回除JSON数据外的任何内容！" + |
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"9.返回的JSON数据不用换行，以最快的速度返回！"; |
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final Message userMsg = Message.builder().role(Role.USER.getValue()).content(questionMsg).build(); |
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msgManager.add(systemMsg); |
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msgManager.add(userMsg); |
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stopWatch.start("开始回答"); |
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final QwenParam param = |
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QwenParam.builder().model("qwen-turbo").messages(msgManager.get()) |
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.resultFormat(QwenParam.ResultFormat.MESSAGE) |
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.seed(1234) |
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.temperature(0.7F) |
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.topP(0.8) |
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.repetitionPenalty(1.0F) |
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.build(); |
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final GenerationResult result = gen.call(param); |
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log.info(result.toString()); |
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// 解析结果
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final String text = result.getOutput().getChoices().get(0).getMessage().getContent(); |
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final String text = callTextFast("You are a helpful assistant.", questionMsg); |
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log.info(text); |
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// 根据{}截取数据
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final int startIndex = text.indexOf('{'); |
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final int endIndex = text.lastIndexOf('}'); |
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@ -871,7 +891,7 @@ public class AliOcrUtil { |
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stopWatch.stop(); |
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log.info(stopWatch.prettyPrint()); |
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jsonObject.put("resultContent", jsonStr); |
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jsonObject.put("msg", result.getOutput().getChoices().get(0).getMessage().getContent()); |
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jsonObject.put("msg", text); |
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return jsonObject; |
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} |
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@ -887,10 +907,6 @@ public class AliOcrUtil { |
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throws NoApiKeyException, ApiException, InputRequiredException { |
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final JSONObject jsonObject = new JSONObject(); |
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Constants.apiKey = "sk-bcfa4865b89548acb8225f910f13d682"; |
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final Generation gen = new Generation(); |
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final MessageManager msgManager = new MessageManager(10); |
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final Message systemMsg = |
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Message.builder().role(Role.SYSTEM.getValue()).content("You are a helpful assistant.").build(); |
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// questionMsg += "请帮我把所有内容封装为JSON，json格式为：[{ \"productSn\": \"货号\", \"productName\": \"名称\" , \"price\":\"单价\",\"attributeList\": [{\"color\":\"颜色\",\"size\":\"尺码\",\"productCount\": \"数量\"}] }]。以下是几点要求： 1.“货号”两个字和\"名称\"两个字中间的内容代表productSn的值,如果没有名称则“货号”两个字和颜色中间的内容代表productSn的值，productSn可能包含\"新\"、\"退\"、\"旧\"、\"换\"、\"补\"。2.\"SYYS色\"代表“所有颜色”，\"color\"字段返回“SYYS色”。 3.\"SYCM码\"代表“所有尺码”，\"size\"字段返回“SYCM码”。4.如果没有名称，productName字段返回\"\"。5.只输出JSON数据即可，不用返回字段描述和解析过程。";
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questionMsg += "你是一个服装行业库管专家，请帮我把所有内容封装为JSON，json格式为：[{ \"productSn\": \"货号\", \"productName\": \"名称\" , \"attributeList\": [{\"color\":\"颜色\",\"size\":\"尺码\",\"productCount\": \"数量\"}], \"purchasePrice\":\"单价\" , \"price\":\"零售价\" , \"wholesalePrice\":\"批发价\" }]\n" + |
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"1.如果没有识别到\"purchasePrice\"的内容，则\"purchasePrice\"赋值\"0\"，purchasePrice为单价！" + |
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@ -903,27 +919,14 @@ public class AliOcrUtil { |
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"8.如果指令中尺码是？码到？码，请完整返回跨度内所有尺码JSON、不要省略中间的尺码。（常见的尺码顺序：S码-M码-L码-XL码-2XL码-3XL码-4XL码-5XL码-6XL码-7XL码......）。\n" + |
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"9.返回的JSON数据不用换行，以最快的速度返回！\n" + |
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"10.只输出JSON数据即可，禁止返回除JSON数据外的任何内容！"; |
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final Message userMsg = Message.builder().role(Role.USER.getValue()).content(questionMsg).build(); |
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msgManager.add(systemMsg); |
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msgManager.add(userMsg); |
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final QwenParam param = |
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QwenParam.builder().model("qwen-turbo").messages(msgManager.get()) |
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.resultFormat(QwenParam.ResultFormat.MESSAGE) |
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.seed(1234) |
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.temperature(0.7F) |
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.topP(0.8) |
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.repetitionPenalty(1.0F) |
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.build(); |
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final GenerationResult result = gen.call(param); |
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log.info(result.toString()); |
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// 解析结果
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final String text = result.getOutput().getChoices().get(0).getMessage().getContent(); |
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final String text = callTextFast("You are a helpful assistant.", questionMsg); |
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log.info(text); |
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// 根据{}截取数据
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final int startIndex = text.indexOf('{'); |
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final int endIndex = text.lastIndexOf('}'); |
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final String jsonStr = '[' + text.substring(startIndex, endIndex + 1) + ']'; |
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jsonObject.put("resultContent", jsonStr); |
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jsonObject.put("msg", result.getOutput().getChoices().get(0).getMessage().getContent()); |
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jsonObject.put("msg", text); |
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return jsonObject; |
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} |
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@ -932,10 +935,6 @@ public class AliOcrUtil { |
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final JSONObject jsonObject = new JSONObject(); |
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Constants.apiKey = "sk-bcfa4865b89548acb8225f910f13d682"; |
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final StopWatch stopWatch = new StopWatch("Ai回答计时"); |
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final Generation gen = new Generation(); |
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final MessageManager msgManager = new MessageManager(10); |
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final Message systemMsg = |
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Message.builder().role(Role.SYSTEM.getValue()).content("You are a helpful assistant.").build(); |
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// questionMsg += "请帮我把所有内容封装为JSON，json格式为：[{ \"productSn\": \"货号\", \"attributeList\": [{\"color\":\"颜色\",\"size\":\"尺码\",\"productCount\": \"数量\"}] }]，只输出JSON数据即可，不用返回字段描述";
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questionMsg += "你是一个服装行业库管专家，请帮我把所有内容封装为JSON，json格式为：[{ \"productSn\": \"货号\", \"attributeList\": [{\"color\":\"颜色\",\"size\":\"尺码\",\"productCount\": \"数量\"}] }]，\n" + |
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"1.productSn的返回值中去掉颜色，尺码等信息，\n" + |
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@ -944,22 +943,9 @@ public class AliOcrUtil { |
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"4.如果指令中有“各N件”，则每个尺码数量返回N。\n" + |
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"5.如果指令中尺码是？码到？码，请完整返回跨度内所有尺码JSON、不要省略中间的尺码。（常见的尺码顺序：S码-M码-L码-XL码-2XL码-3XL码-4XL码-5XL码-6XL码-7XL码......）。\n" + |
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"6.只输出JSON数据即可，禁止返回除JSON数据外的任何内容！"; |
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final Message userMsg = Message.builder().role(Role.USER.getValue()).content(questionMsg).build(); |
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msgManager.add(systemMsg); |
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msgManager.add(userMsg); |
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stopWatch.start("开始回答"); |
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final QwenParam param = |
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QwenParam.builder().model("qwen-turbo").messages(msgManager.get()) |
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.resultFormat(QwenParam.ResultFormat.MESSAGE) |
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.seed(1234) |
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.temperature(0.85F) |
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.topP(0.8) |
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.repetitionPenalty(1.0F) |
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.build(); |
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final GenerationResult result = gen.call(param); |
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log.info(result.toString()); |
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// 解析结果
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|
final String text = result.getOutput().getChoices().get(0).getMessage().getContent(); |
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final String text = callTextFast("You are a helpful assistant.", questionMsg); |
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log.info(text); |
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|
// 根据{}截取数据
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final int startIndex = text.indexOf('{'); |
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final int endIndex = text.lastIndexOf('}'); |
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@ -968,7 +954,7 @@ public class AliOcrUtil { |
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stopWatch.stop(); |
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log.info(stopWatch.prettyPrint()); |
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jsonObject.put("resultContent", jsonStr); |
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jsonObject.put("msg", result.getOutput().getChoices().get(0).getMessage().getContent()); |
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|
jsonObject.put("msg", text); |
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return jsonObject; |
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} |
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@ -1218,23 +1204,10 @@ public class AliOcrUtil { |
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final JSONObject jsonObject = new JSONObject(); |
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Constants.apiKey = "sk-bcfa4865b89548acb8225f910f13d682"; |
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|
final StopWatch stopWatch = new StopWatch("Ai回答计时"); |
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|
final Generation gen = new Generation(); |
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|
final MessageManager msgManager = new MessageManager(10); |
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|
final Message systemMsg = |
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Message.builder().role(Role.SYSTEM.getValue()).content("You are a helpful assistant.").build(); |
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|
questionMsg += "请帮我把所有内容封装为JSON，json格式为：[{ \"senderName\": \"发货人\", \"senderPhone\": \"发货人电话\" , \"receiverName\": \"收货人\", \"receiverPhone\": \"收货人电话\" , \"arrivalStation\":\"\"到达站,\"weight\":\"重量\",\"count\":\"件数\",\"methodOfSettlement\":\"结算方式\",\"tips\":\"小费\" }]。没有识别到的信息JSON字段返回\"\"。"; |
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|
final Message userMsg = Message.builder().role(Role.USER.getValue()).content(questionMsg).build(); |
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|
msgManager.add(systemMsg); |
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|
msgManager.add(userMsg); |
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|
stopWatch.start("开始回答"); |
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|
final QwenParam param = |
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|
QwenParam.builder().model("qwen-max").messages(msgManager.get()) |
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|
.resultFormat(QwenParam.ResultFormat.MESSAGE) |
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|
.build(); |
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|
final GenerationResult result = gen.call(param); |
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|
log.info(result.toString()); |
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|
|
// 解析结果
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|
|
final String text = result.getOutput().getChoices().get(0).getMessage().getContent(); |
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|
|
final String text = callTextFast("You are a helpful assistant.", questionMsg); |
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|
log.info(text); |
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|
|
// 根据{}截取数据
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|
final int startIndex = text.indexOf('{'); |
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|
final int endIndex = text.lastIndexOf('}'); |
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@ -1242,7 +1215,7 @@ public class AliOcrUtil { |
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|
stopWatch.stop(); |
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log.info(stopWatch.prettyPrint()); |
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|
jsonObject.put("resultContent", jsonStr); |
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|
jsonObject.put("msg", result.getOutput().getChoices().get(0).getMessage().getContent()); |
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|
|
jsonObject.put("msg", text); |
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|
|
return jsonObject; |
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|
|
} |
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|
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|
|
@ -1258,10 +1231,6 @@ public class AliOcrUtil { |
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|
|
final JSONObject jsonObject = new JSONObject(); |
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|
|
Constants.apiKey = "sk-bcfa4865b89548acb8225f910f13d682"; |
|
|
|
final StopWatch stopWatch = new StopWatch("Ai回答计时"); |
|
|
|
final Generation gen = new Generation(); |
|
|
|
final MessageManager msgManager = new MessageManager(10); |
|
|
|
final Message systemMsg = |
|
|
|
Message.builder().role(Role.SYSTEM.getValue()).content("你是一个物流开票员（这句话是给你的角色设定，不要在json信息中返回）").build(); |
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|
|
final List<LogisticsAddressBook> shipperLogisticsAddressBooks = logisticsAddressBooks.get("shipper"); |
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|
|
final List<LogisticsAddressBook> receiverLogisticsAddressBooks = logisticsAddressBooks.get("receiver"); |
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|
|
final StringBuilder endQuestionMsg = new StringBuilder(); |
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|
|
@ -1295,18 +1264,9 @@ public class AliOcrUtil { |
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|
|
.append("4.weight、count、tips、premium这几个字段只返回数字; ") |
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|
|
.append("5.不要返回推理过程，只输出1条JSON数据即可，禁止返回除JSON数据外的任何内容！返回的JSON数据不用换行，以最快的速度返回！"); |
|
|
|
log.info("AI物流最终指令==="+endQuestionMsg); |
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|
|
final Message userMsg = Message.builder().role(Role.USER.getValue()).content(endQuestionMsg.toString()).build(); |
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|
|
msgManager.add(systemMsg); |
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|
|
msgManager.add(userMsg); |
|
|
|
stopWatch.start("开始回答"); |
|
|
|
final QwenParam param = |
|
|
|
QwenParam.builder().model("qwen-max").messages(msgManager.get()) |
|
|
|
.resultFormat(QwenParam.ResultFormat.MESSAGE) |
|
|
|
.build(); |
|
|
|
final GenerationResult result = gen.call(param); |
|
|
|
log.info(result.toString()); |
|
|
|
// 解析结果
|
|
|
|
final String text = result.getOutput().getChoices().get(0).getMessage().getContent(); |
|
|
|
final String text = callTextFast("你是一个物流开票员（这句话是给你的角色设定，不要在json信息中返回）", endQuestionMsg.toString()); |
|
|
|
log.info(text); |
|
|
|
// 根据{}截取数据
|
|
|
|
final int startIndex = text.indexOf('{'); |
|
|
|
final int endIndex = text.lastIndexOf('}'); |
|
|
|
@ -1314,7 +1274,7 @@ public class AliOcrUtil { |
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|
|
stopWatch.stop(); |
|
|
|
log.info(stopWatch.prettyPrint()); |
|
|
|
jsonObject.put("resultContent", jsonStr); |
|
|
|
jsonObject.put("msg", result.getOutput().getChoices().get(0).getMessage().getContent()); |
|
|
|
jsonObject.put("msg", text); |
|
|
|
return jsonObject; |
|
|
|
} |
|
|
|
} |
wangfukang
3 days ago