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know_sub
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优化

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dev_1.0.0^2
liu 8 months ago
parent 12d94a2cc5
commit c5182c9e78
5 changed files with 7 additions and 109 deletions
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3
know_sub_etl/pom.xml
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@ -30,9 +30,8 @@
<version>4.5.13</version>
</dependency>
<!-- 引入ollama的依赖.版本号来自于 dependencyManagement中 spring-ai-bom中的版本号.-->
<dependency>
<groupId>io.springboot.ai</groupId>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
</dependency>

8
know_sub_rag/pom.xml
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@ -30,9 +30,13 @@
<version>4.5.13</version>
</dependency>
<!-- 引入ollama的依赖.版本号来自于 dependencyManagement中 spring-ai-bom中的版本号.-->
<!-- <dependency>-->
<!-- <groupId>io.springboot.ai</groupId>-->
<!-- <artifactId>spring-ai-ollama-spring-boot-starter</artifactId>-->
<!-- </dependency>-->
<dependency>
<groupId>io.springboot.ai</groupId>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-ollama-spring-boot-starter</artifactId>
</dependency>

35
know_sub_rag/src/main/java/com/supervision/knowsub/config/ElasticsearchVectorStoreConfig.java
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@ -1,35 +0,0 @@
package com.supervision.knowsub.config;
import org.elasticsearch.client.RestClient;
import org.springframework.ai.autoconfigure.vectorstore.elasticsearch.ElasticsearchVectorStoreProperties;
import org.springframework.ai.embedding.EmbeddingClient;
import org.springframework.ai.embedding.EmbeddingModel;
import org.springframework.ai.vectorstore.ElasticsearchVectorStore;
import org.springframework.ai.vectorstore.ElasticsearchVectorStoreOptions;
import org.springframework.boot.autoconfigure.condition.ConditionalOnProperty;
import org.springframework.boot.context.properties.EnableConfigurationProperties;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.util.Assert;
@Configuration
@EnableConfigurationProperties(EmbeddingProperties.class)
public class ElasticsearchVectorStoreConfig {
@Bean
@ConditionalOnProperty(prefix = "embedding", name = "url")
public EmbeddingModel embeddingModel(EmbeddingProperties embeddingProperties) {
Assert.notNull(embeddingProperties.getUrl(), "配置文件embedding:url未找到");
return new VectorEmbeddingModel(embeddingProperties.getUrl());
}
@Bean
@ConditionalOnProperty(prefix = "embedding", name = "url")
public ElasticsearchVectorStore vectorStore(ElasticsearchVectorStoreProperties properties, EmbeddingModel embeddingModel, RestClient restClient) {
ElasticsearchVectorStoreOptions options = new ElasticsearchVectorStoreOptions();
options.setIndexName(properties.getIndexName());
options.setDimensions(1024);
return new ElasticsearchVectorStore(options, restClient, embeddingModel, true);
}
}

13
know_sub_rag/src/main/java/com/supervision/knowsub/config/EmbeddingProperties.java
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@ -1,13 +0,0 @@
package com.supervision.knowsub.config;
import lombok.Data;
import org.springframework.boot.context.properties.ConfigurationProperties;
import org.springframework.boot.context.properties.EnableConfigurationProperties;
@Data
@ConfigurationProperties(prefix = "embedding")
public class EmbeddingProperties {
private String url;
}

57
know_sub_rag/src/main/java/com/supervision/knowsub/config/VectorEmbeddingModel.java
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@ -1,57 +0,0 @@
package com.supervision.knowsub.config;
import cn.hutool.http.HttpUtil;
import cn.hutool.json.JSONUtil;
import lombok.Data;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.document.Document;
import org.springframework.ai.embedding.*;
import org.springframework.util.Assert;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.concurrent.atomic.AtomicInteger;
@Slf4j
public class VectorEmbeddingModel implements EmbeddingModel {
private final String embeddingUrl;
public VectorEmbeddingModel(String embeddingUrl) {
this.embeddingUrl = embeddingUrl;
}
@Override
public List<Double> embed(Document document) {
List<List<Double>> list = this.call(new EmbeddingRequest(List.of(document.getContent()), EmbeddingOptions.EMPTY))
.getResults()
.stream()
.map(Embedding::getOutput)
.toList();
return list.iterator().next();
}
@Override
public EmbeddingResponse call(EmbeddingRequest request) {
Assert.notEmpty(request.getInstructions(), "At least one text is required!");
List<List<Double>> embeddingList = new ArrayList<>();
for (String inputContent : request.getInstructions()) {
// 这里需要吧inputContent转化为向量数据
String post = HttpUtil.post(embeddingUrl, JSONUtil.toJsonStr(Map.of("text", inputContent)));
EmbeddingData bean = JSONUtil.toBean(post, EmbeddingData.class);
embeddingList.add(bean.embeddings);
}
var indexCounter = new AtomicInteger(0);
List<Embedding> embeddings = embeddingList.stream()
.map(e -> new Embedding(e, indexCounter.getAndIncrement()))
.toList();
return new EmbeddingResponse(embeddings);
}
@Data
private static class EmbeddingData {
private List<Double> embeddings;
}
}
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