上传备份

pom.xml

@@ -77,11 +77,11 @@
             <artifactId>langchain4j-embeddings</artifactId>
             <version>${langchain4j.version}</version>
         </dependency>
-        <dependency>
-            <groupId>dev.langchain4j</groupId>
-            <artifactId>langchain4j-embeddings-all-minilm-l6-v2</artifactId>
-            <version>${langchain4j.version}</version>
-        </dependency>
+<!--        <dependency>-->
+<!--            <groupId>dev.langchain4j</groupId>-->
+<!--            <artifactId>langchain4j-embeddings-all-minilm-l6-v2</artifactId>-->
+<!--            <version>${langchain4j.version}</version>-->
+<!--        </dependency>-->
 
         <dependency>
             <groupId>dev.langchain4j</groupId>
@@ -102,6 +102,13 @@
             <version>1.0.0-beta2</version>
         </dependency>
 
+        <!-- 然语言处理工具包 -->
+        <dependency>
+            <groupId>com.hankcs</groupId>
+            <artifactId>hanlp</artifactId>
+            <version>portable-1.8.4</version>
+        </dependency>
+
         <!-- 日志框架 -->
         <dependency>
             <groupId>ch.qos.logback</groupId>

src/main/java/xyz/wbsite/ai/HanLP_Example.java

@@ -0,0 +1,44 @@
+package xyz.wbsite.ai;
+
+import com.hankcs.hanlp.HanLP;
+import com.hankcs.hanlp.corpus.tag.Nature;
+import com.hankcs.hanlp.seg.common.Term;
+import com.hankcs.hanlp.tokenizer.StandardTokenizer;
+
+import java.util.List;
+
+public class HanLP_Example {
+    public static void main(String[] args) {
+
+//        HanLPTokenizer hanLPTokenizer = new HanLPTokenizer();
+//        String[] segment = hanLPTokenizer.segment("我喜欢吃苹果");
+//
+//        List<String> 我喜欢吃苹果1 = HanLP.extractKeyword("我喜欢吃苹果", 2);
+//
+//        HanLPEngine hanLPEngine = new HanLPEngine();
+//        Result parse = hanLPEngine.parse("我喜欢吃苹果");
+//
+//
+//        for (Word word : parse) {
+//            System.out.println(word);
+//        }
+
+        List<String> strings = HanLP.extractKeyword("身份证去哪里办理", 1);
+        List<String> string = HanLP.extractKeyword("需要带什么材料", 1);
+
+        // 分词
+        List<List<Term>> lists = StandardTokenizer.seg2sentence("那么还需要哪些材料");
+        for (List<Term> list : lists) {
+            for (Term term : list) {
+                System.out.println(term);
+                // 检查词性是否为主语相关的词性（例如：主谓宾中的主语通常是名词或代词）
+//                if (term.nature.equals(Nature.n)) { // 名词
+//                    System.out.println("主语: " + term);
+//                }
+//                if (term.nature.equals(Nature.r)) { // 代词，例如“他”、“她”等
+//                    System.out.println("主语: " + term);
+//                }
+            }
+        }
+    }
+}

src/main/java/xyz/wbsite/ai/Helper.java

@@ -3,6 +3,7 @@ package xyz.wbsite.ai;
 import cn.hutool.core.collection.CollUtil;
 import dev.langchain4j.data.document.Document;
 import dev.langchain4j.model.openai.OpenAiChatModel;
+import dev.langchain4j.model.openai.OpenAiEmbeddingModel;
 import dev.langchain4j.model.openai.OpenAiStreamingChatModel;
 
 import java.util.List;
@@ -25,6 +26,14 @@ public class Helper {
             .logResponses(true)
             .build();
 
+    private static OpenAiEmbeddingModel openAiEmbeddingModel = OpenAiEmbeddingModel.builder()
+            .baseUrl("http://192.168.88.106:11434/v1")
+            .apiKey("1")
+            .modelName("bge-m3")
+            .logRequests(true)
+            .logResponses(true)
+            .build();
+
     private static  OpenAiChatModel toolChatModel = OpenAiChatModel.builder()
             .baseUrl("http://192.168.88.106:11434/v1")
             .apiKey("1")
@@ -53,6 +62,10 @@ public class Helper {
         return gemmaModel;
     }
 
+    public static OpenAiEmbeddingModel getOpenAiEmbeddingModel() {
+        return openAiEmbeddingModel;
+    }
+
     public static Document getDocument() {
         return Document.from("人往往在做梦的时候会打呼噜");
     }

src/main/java/xyz/wbsite/ai/Qdrant_Embedding_Example.java

@@ -3,7 +3,7 @@ package xyz.wbsite.ai;
 import dev.langchain4j.data.embedding.Embedding;
 import dev.langchain4j.data.segment.TextSegment;
 import dev.langchain4j.model.embedding.EmbeddingModel;
-import dev.langchain4j.model.embedding.onnx.allminilml6v2.AllMiniLmL6V2EmbeddingModel;
+import dev.langchain4j.model.embedding.onnx.bgesmallenv15q.BgeSmallEnV15QuantizedEmbeddingModel;
 import dev.langchain4j.store.embedding.EmbeddingStore;
 import dev.langchain4j.store.embedding.qdrant.QdrantEmbeddingStore;
 
@@ -22,7 +22,7 @@ public class Qdrant_Embedding_Example {
                         .collectionName("langchain4j-" + randomUUID())
                         .build();
 
-        EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
+        EmbeddingModel embeddingModel = new BgeSmallEnV15QuantizedEmbeddingModel();
         TextSegment segment1 = TextSegment.from("I've been to France twice.");
         Embedding embedding1 = embeddingModel.embed(segment1).content();
         embeddingStore.add(embedding1, segment1);

src/main/java/xyz/wbsite/ai/Text_Compare_Example.java

@@ -2,7 +2,6 @@ package xyz.wbsite.ai;
 
 import dev.langchain4j.data.embedding.Embedding;
 import dev.langchain4j.model.embedding.EmbeddingModel;
-import dev.langchain4j.model.embedding.onnx.allminilml6v2.AllMiniLmL6V2EmbeddingModel;
 import dev.langchain4j.model.embedding.onnx.bgesmallenv15q.BgeSmallEnV15QuantizedEmbeddingModel;
 import dev.langchain4j.store.embedding.CosineSimilarity;
 
@@ -13,11 +12,12 @@ public class Text_Compare_Example {
 
     public static void main(String[] args) {
         // 初始化嵌入模型
-        EmbeddingModel embeddingModel = new AllMiniLmL6V2EmbeddingModel();
+//        EmbeddingModel embeddingModel = new BgeSmallEnV15QuantizedEmbeddingModel();
+        EmbeddingModel embeddingModel = Helper.getOpenAiEmbeddingModel();
 
         // 将文本转换为向量
-        Embedding embedding1 = embeddingModel.embed("工伤医疗费的申领").content();
-        Embedding embedding2 = embeddingModel.embed("预告登记的转移").content();
+        Embedding embedding1 = embeddingModel.embed("身份证办理").content();
+        Embedding embedding2 = embeddingModel.embed("身份证首次办理").content();
 
         double between = CosineSimilarity.between(embedding1, embedding2);
         System.out.println("余弦相似度: " + between); // 值越接近1越相似