diff --git a/rag/DWALK商城上架商品.txt b/rag/DWALK商城上架商品.txt new file mode 100644 index 0000000..5597e08 --- /dev/null +++ b/rag/DWALK商城上架商品.txt @@ -0,0 +1,6 @@ +DWALK商城上架商品 +商品名称：机械革命旷世G16蛟龙16k/15K/15极光16pro定制4060游戏笔记本电脑，价格：8000元，店铺：机械革命官方旗舰店，商品分类：电脑/笔记本电脑 简介：32G，显卡型号NVIDIA GeForce RTX4060，CPU型号英特尔 酷睿 i7-13650HX，学生,家庭娱乐,高清游戏,设计制图，办公 +商品名称：小米RedmiBook 16 2024英特尔酷睿标压轻薄商务笔记本办公商务电脑，价格：3000元，店铺：小米官方旗舰店，商品分类：电脑/笔记本电脑 简介：16G，显卡型号UHD Graphics，CPU型号英特尔 酷睿 i5-12450H，商务办公 女性定位 家庭影音 +商品名称：ALIENWARE外星人m16 R2 Ultra，价格：12000元，店铺：外星人长虹佳华专卖店，商品分类：电脑/笔记本电脑 简介：16G，显卡型号NVIDIA GeForce RTX4060，CPU型号英特尔 酷睿 Ultra7 155H，AI助力高性能本，高清游戏 +商品名称：HONOR/荣耀X60 5G智能手机，价格：1146元，店铺：荣耀官方旗舰店，商品分类：手机/智能手机 简介：一亿像素，6.8英寸，电池容量5800mAh高能量密度大电池超耐久使用，十面耐摔防水抗震，512GB超大存储空间，AI智慧助手魔法抠图 +商品名称：REDMI K80红米k80，价格：2449元，店铺：小米官方旗舰店，商品分类：手机/智能手机 简介：Android/安卓系统，5000万像素，6.67英寸，电池容量6550mAh智慧充电引擎，性能满贯，狂暴进化，AI从芯片低层深度赋能，实现性能实力爆发，轻松应对游戏中高负载场景 diff --git a/src/main/java/com/ai/config/SegmentConfig.java b/src/main/java/com/ai/config/SegmentConfig.java new file mode 100644 index 0000000..d07451e --- /dev/null +++ b/src/main/java/com/ai/config/SegmentConfig.java @@ -0,0 +1,68 @@ +package com.ai.config; + +import com.ai.service.SegmentAssist; +import dev.langchain4j.community.model.dashscope.QwenChatModel; +import dev.langchain4j.community.model.dashscope.QwenEmbeddingModel; +import dev.langchain4j.data.document.Document; +import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; +import dev.langchain4j.data.document.splitter.DocumentByCharacterSplitter; +import dev.langchain4j.data.document.splitter.DocumentByLineSplitter; +import dev.langchain4j.data.embedding.Embedding; +import dev.langchain4j.data.segment.TextSegment; +import dev.langchain4j.memory.chat.MessageWindowChatMemory; +import dev.langchain4j.model.chat.ChatLanguageModel; +import dev.langchain4j.model.output.Response; +import dev.langchain4j.rag.content.retriever.ContentRetriever; +import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever; +import dev.langchain4j.service.AiServices; +import dev.langchain4j.store.embedding.EmbeddingMatch; +import dev.langchain4j.store.embedding.EmbeddingSearchRequest; +import dev.langchain4j.store.embedding.EmbeddingSearchResult; +import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; +import lombok.RequiredArgsConstructor; +import org.springframework.beans.factory.annotation.Value; +import org.springframework.context.annotation.Bean; +import org.springframework.context.annotation.Configuration; + +import java.util.List; + +@Configuration +@RequiredArgsConstructor +public class SegmentConfig { + + @Value("${langchain4j.api-key}") + private String apiKey; + + @Value("${langchain4j.model}") + private String model; + + @Bean + public SegmentAssist segmentAssist() { + ChatLanguageModel qwenModel = QwenChatModel.builder() + .apiKey(apiKey) + .modelName(model) + .build(); + QwenEmbeddingModel embeddingModel = QwenEmbeddingModel.builder().apiKey(apiKey).build(); + InMemoryEmbeddingStore embeddingStore = new InMemoryEmbeddingStore<>(); + List documents = FileSystemDocumentLoader.loadDocuments("E:\\ideaProject\\liang-ai\\rag"); + for (Document document : documents) { + DocumentByLineSplitter splitter = new DocumentByLineSplitter(300,30); + List segments = splitter.split(document); + List embeddings = embeddingModel.embedAll(segments).content(); + embeddingStore.addAll(embeddings, segments); + } + + ContentRetriever contentRetriever = EmbeddingStoreContentRetriever.builder() + .embeddingStore(embeddingStore) + .embeddingModel(embeddingModel) + .maxResults(5) + .minScore(0.6) + .build(); + // for simplicity, we will use an in-memory one: + return AiServices.builder(SegmentAssist.class) + .chatMemoryProvider(memoryId -> MessageWindowChatMemory.withMaxMessages(10)) + .chatLanguageModel(qwenModel) + .contentRetriever(contentRetriever) + .build(); + } +} diff --git a/src/main/java/com/ai/controller/LangChainController.java b/src/main/java/com/ai/controller/LangChainController.java index 9ff1a31..ca95586 100644 --- a/src/main/java/com/ai/controller/LangChainController.java +++ b/src/main/java/com/ai/controller/LangChainController.java @@ -1,9 +1,6 @@ package com.ai.controller; -import com.ai.service.Assist; -import com.ai.service.LangChainService; -import com.ai.service.NormalRequestService; -import com.ai.service.OllamaAssist; +import com.ai.service.*; import dev.langchain4j.community.model.dashscope.QwenChatModel; import dev.langchain4j.community.model.zhipu.ZhipuAiImageModel; import dev.langchain4j.data.image.Image; @@ -39,6 +36,12 @@ public class LangChainController { @Autowired private NormalRequestService normalRequestService; + @Autowired + private SegmentAssist segmentAssist; + + @Autowired + private EmbeddingService embeddingService; + @GetMapping("/normal/chat") public String normalChat(@RequestParam("input") String input) { System.out.println("start normal chat..."); @@ -81,4 +84,11 @@ public class LangChainController { System.out.println("start highlevel memory chat..."); return ollamaAssist.chat(input); } + + @GetMapping("/embedd/chat") + public String embeddChat(@RequestParam("input") String input) { + System.out.println("start embedd chat..."); + embeddingService.embedding(input); + return segmentAssist.chat(input); + } } diff --git a/src/main/java/com/ai/http/chat.http b/src/main/java/com/ai/http/chat.http index 2306d78..1f97717 100644 --- a/src/main/java/com/ai/http/chat.http +++ b/src/main/java/com/ai/http/chat.http @@ -16,3 +16,6 @@ GET http://localhost:8080/langchain/zhipu/img?input=请画一张魔兽世界里 ### 测试 LangChainController 的 highlevel chat 接口 GET http://localhost:8080/langchain/high/call?input=用5个菲林抽取5个代理人 + +### 测试 LangChainController 的 highlevel chat 接口 +GET http://localhost:8080/langchain/embedd/chat?input=我想要个便宜的办公笔记本，有什么推荐吗 diff --git a/src/main/java/com/ai/service/EmbeddingService.java b/src/main/java/com/ai/service/EmbeddingService.java new file mode 100644 index 0000000..b0924c4 --- /dev/null +++ b/src/main/java/com/ai/service/EmbeddingService.java @@ -0,0 +1,42 @@ +package com.ai.service; + +import dev.langchain4j.community.model.dashscope.QwenEmbeddingModel; +import dev.langchain4j.data.document.Document; +import dev.langchain4j.data.document.loader.FileSystemDocumentLoader; +import dev.langchain4j.data.document.splitter.DocumentByLineSplitter; +import dev.langchain4j.data.embedding.Embedding; +import dev.langchain4j.data.segment.TextSegment; +import dev.langchain4j.model.output.Response; +import dev.langchain4j.store.embedding.EmbeddingMatch; +import dev.langchain4j.store.embedding.EmbeddingSearchRequest; +import dev.langchain4j.store.embedding.EmbeddingSearchResult; +import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore; +import org.springframework.beans.factory.annotation.Value; +import org.springframework.stereotype.Service; + +import java.util.List; + +@Service +public class EmbeddingService { + + @Value("${langchain4j.api-key}") + private String apiKey; + + public void embedding(String input) { + QwenEmbeddingModel embeddingModel = QwenEmbeddingModel.builder().apiKey(apiKey).build(); + InMemoryEmbeddingStore embeddingStore = new InMemoryEmbeddingStore<>(); + List documents = FileSystemDocumentLoader.loadDocuments("E:\\ideaProject\\liang-ai\\rag"); + for (Document document : documents) { + DocumentByLineSplitter splitter = new DocumentByLineSplitter(200,30); + List segments = splitter.split(document); + List embeddings = embeddingModel.embedAll(segments).content(); + embeddingStore.addAll(embeddings, segments); + Response embed = embeddingModel.embed(input); + EmbeddingSearchRequest builder = EmbeddingSearchRequest.builder().queryEmbedding(embed.content()).build(); + EmbeddingSearchResult results = embeddingStore.search(builder); + for (EmbeddingMatch match : results.matches()) { + System.out.println(match.embedded().text() + ", 分数为" + match.score()); + } + } + } +} diff --git a/src/main/java/com/ai/service/SegmentAssist.java b/src/main/java/com/ai/service/SegmentAssist.java new file mode 100644 index 0000000..b673367 --- /dev/null +++ b/src/main/java/com/ai/service/SegmentAssist.java @@ -0,0 +1,9 @@ +package com.ai.service; + +import dev.langchain4j.service.SystemMessage; + +public interface SegmentAssist { + + @SystemMessage("你是DWALK商城助手，你需要基于DWALK商城上架的商品来回答问题") + String chat(String userMessage); +} diff --git a/个人知识库 b/个人知识库 index 8507bd9..60e3a5f 100644 --- a/个人知识库 +++ b/个人知识库 @@ -1,18 +1,4 @@ 梁靖林个人博客halo 地址：halo.liangjl.top 账号：liangjinglin -密码: 353912918a - -DWALK商城 商品推荐优先级规则 -1.根据本月销量推荐，销量越高，优先级越高，考量占比50% -2.根据评分，评分越高，优先级越高 考量占比50% -3.根据评论数，评论数越多，优先级越高 考量占比10% -4.根据收藏数，收藏数越多，优先级越高 考量占比5% - -DWALK商城商品情况 -1.茅台 销量：100 评分：4 评论数：100 收藏数：100 -2.meta50 销量：200 评分：5 评论数：500 收藏数：5000 -3.机械革命 销量：50 评分：4.5 评论数：80 收藏数：100 -4.潮汕牛肉丸 销量：150 评分：3 评论数：100 收藏数：20 -5.风筒 销量：80 评分：3.5 评论数：50 收藏数：10 -6.口红 销量：30 评分：4.5 评论数：200 收藏数：200 \ No newline at end of file +密码: 353912918a \ No newline at end of file