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向量检索版支持RAG方案

更新时间:

本文介绍如何使用OpenSearch智能问答版,为OpenSearch向量检索版提供RAG能力。

假设用户已经购买向量检索版实例,则只需购买一个智能问答版实例,这两个实例的具体作用如下:

向量检索版实例负责:

  • 存储用户原始文档数据,向量数据

  • 召回用户原始文档数据,向量数据

智能问答版实例负责:

  • 对用户原始文档进行切片和向量化(非必须)

  • 对用户原始query进行向量化 (非必须)

  • 对召回结果进行推理总结

具体操作流程如下图所示:

image

1. 创建和配置实例

1.1. 创建和配置向量检索版实例

1.1.1. 购买OpenSearch向量检索版实例

如果用户已经有了向量检索版实例,则无需再购买新的实例。

购买实例可参考购买OpenSearch向量检索版实例

1.1.2. 配置OpenSearch向量检索版实例

如果用户不想修改现有向量检索版实例的查询和召回逻辑,则无需对现有实例配置进行修改,只需调用智能问答版的问答接口,对现有召回结果进行推理和总结(可直接阅读3.3章节)。

新购买的实例,其状态为“待配置”,之后需要为该实例配置一张表,如下图所示,点击配置,进入表配置流程。

image.png

1.1.2.1. 表基础信息

image.png

配置说明:

表名称:可自定义

数据分片数:分片数设置时,各索引表分片数需保持一致;或至少一个索引表分片数为1,其余索引表分片数一致

数据更新资源数:数据更新所用资源数,每个索引默认免费提供2个4核8G的更新资源,超出免费额度的资源将产生费用,详情可参考向量检索版计费概述

场景模板:选择“通用模板”

点击下一步,进入数据同步配置

1.1.2.2. 数据同步配置

image.png

配置说明:

数据源类型:选择API推送数据源,表示用户的数据通过API的方式推送至实例中

点击下一步进入字段配置

1.1.2.3. 字段配置

image.png

表结构中必须包含以下字段:

  • 文档主键,如上图中的doc_id;

  • 文档切分后内容字段,如上图中的split_content;

  • 文档切分后的内容向量化值,如上图中的split_content_embedding。该字段需要设置为向量字段,并以","分割。

除了上述必须字段外,客户可以自定义添加其他业务字段,并使用这些字段做排序、过滤等,来满足客户的业务需求。

1.1.2.4. 索引结构

image.png

配置说明:

向量维度:需要改成1536维

其他参数:保持默认即可

1.1.2.5. 确认创建

image.png

点击确认创建,即可开始表的创建。

可在变更历史中查看表创建进度,进度完成后,说明表已经就绪,可以进行后续流程。

image.png

1.2. 创建和配置智能问答版实例

1.2.1. 购买和配置OpenSearch智能问答版实例

购买实例可参考企业知识库问答

智能问答版实例无需特殊配置,实例购买完成即可使用。

2. 数据入库

2.1. 对文档内容进行切片和向量化

当前向量化模型最大支持300个token,而用户原始文档内容通常会比较大,因此需要对用户原始文档内容进行切片。并且还需要对切片之后的文档内容进行向量化。

调用智能问答版实例相关endpoint和api,请参考本文末尾的JAVA SDK Demo,或者参考文章:

SplitDoc-文本切片及向量化

2.2. 将切片和向量化之后的文档推送到向量检索版实例

在2.1返回结果中:

  • chunk_id为切分后的文档ID。需要和原始文档ID拼接起来,组成新的文档主键:doc_id

  • chunk为切分后文档内容,对应split_content;

  • embedding为切分后文档内容向量化值,对应split_content_embedding;

需要将上述结果推送到向量检索版实例。

调用向量检索版实例相关endpoint和api,请参考本文末尾的JAVA SDK Demo,或者参考文章:

数据推送 Demo

3. 查询问答

3.1. query向量化

将用户原始查询内容进行向量化。

调用智能问答版实例相关endpoint和api,请参考本文末尾的JAVA SDK Demo,或者参考文章:

EmbeddingDoc-文本向量化

3.2. 召回向量化结果数据

使用query的向量化结果,去向量检索版进行召回。通常来说,召回top5的结果即可。

调用向量检索版实例相关endpoint和api,请参考本文末尾的JAVA SDK Demo,或者参考文章:

文档搜索 Demo

3.3. 大模型推理总结

基于向量检索版召回的结果,调用智能问答版的大模型文档接口,进行推理总结。

请参考本文末尾的JAVA SDK Demo,或者参考文章:

KnowledgeLLM-大模型对话接口

4. JAVA SDK Demo

使用Java SDK完成数据推送和智能问答的demo如下:

添加Maven依赖:

<dependency>
 <groupId>com.aliyun</groupId>
 <artifactId>aliyun-sdk-ha3engine-vector</artifactId>
 <version>1.1.1</version>
</dependency>
<dependency>
 <groupId>com.aliyun.opensearch</groupId>
 <artifactId>aliyun-sdk-opensearch</artifactId>
 <version>4.0.0</version>
</dependency>

JAVA Demo:

import com.aliyun.ha3engine.vector.Client;
import com.aliyun.ha3engine.vector.models.*;
import com.aliyun.opensearch.OpenSearchClient;
import com.aliyun.opensearch.sdk.dependencies.org.json.JSONObject;
import com.aliyun.opensearch.sdk.generated.OpenSearch;
import com.aliyun.opensearch.sdk.generated.commons.OpenSearchClientException;
import com.aliyun.opensearch.sdk.generated.commons.OpenSearchException;
import com.aliyun.opensearch.sdk.generated.commons.OpenSearchResult;
import com.google.gson.JsonArray;
import com.google.gson.JsonElement;
import com.google.gson.JsonObject;
import com.google.gson.JsonParser;

import java.util.*;

public class LLMDemo {

 /**
 * 智能问答版实例应用名称
 */
 private static String llmAppName = "test_llm";
 /**
 * 智能问答版实例访问地址
 */
 private static String llmHost = "http://opensearch-cn-shanghai.aliyuncs.com";
 /**
 * 智能问答版实例访问key
 */
 private static String llmAccessKey = "xxxxx";
 /**
 * 智能问答版实例访问密钥
 */
 private static String llmAccessSecret = "xxxx";



 /**
 * 向量检索版实例API域名
 */
 private static String embeddingEndpoint = "ha-cn-xxxx.public.ha.aliyuncs.com";
 /**
 * 向量检索版实例名称
 */
 private static String embeddingInstanceId = "ha-cn-xxx";
 /**
 * 向量检索版文档数据表名称
 */
 private static String embeddingTableName = "ha-cn-xxx_xxx";


 /**
 * 向量检索版文档索引表名称
 */
 private static String embeddingIndexName = "xxx";
 /**
 * 向量检索版文档推送的文档主键字段.
 */
 private static String embeddingPkField = "doc_id";
 /**
 * 向量检索版用户名
 */
 private static String embeddingUserName = "xxxx";
 /**
 * 向量检索版密码
 */
 private static String embeddingPassword = "xxxx";



 public static void main(String[] args) throws Exception {

 //创建访问智能问答版实例的对象
 //创建并构造OpenSearch对象
 OpenSearch openSearch = new OpenSearch(llmAccessKey, llmAccessSecret, llmHost);
 //创建OpenSearchClient对象,并以OpenSearch对象作为构造参数
 OpenSearchClient llmClient = new OpenSearchClient(openSearch);

 //创建访问向量检索版实例的对象
 Config config = new Config();
 config.setEndpoint(embeddingEndpoint);
 config.setInstanceId(embeddingInstanceId);
 config.setAccessUserName(embeddingUserName);
 config.setAccessPassWord(embeddingPassword);
 Client embeddingClient = new Client(config);



 //对content进行切分和向量化
 Map<String, String> splitParams = new HashMap<String, String>() {{
 put("format", "full_json");
 put("_POST_BODY", "{\"content\":\"OpenSearch是基于阿里巴巴自主研发的大规模分布式搜索引擎搭建的一站式商用智能搜索平台,目前为包括淘宝、天猫、菜鸟在内的阿里集团核心搜索业务提供中台服务支持。" +
 "经过多年的行业搜索经验沉淀、双11大促流量冲击,智能开放搜索OpenSearch打磨出一套高性能、高时效、高可用、强稳定搜索全家桶服务,包括LLM智能问答版、行业算法版、高性能检索版、向量检索版、召回引擎版五类商品版本,以满足各行各业的搜索需求。" +
 "OpenSearch以平台服务化的形式,将专业搜索技术简单化、低门槛化和低成本化,让搜索不再成为客户的业务瓶颈,以低成本实现产品搜索功能并快速迭代\",\"use_embedding\":true}");
 }};
 String splitPath = String.format("/apps/%s/actions/knowledge-split", llmAppName);
 OpenSearchResult openSearchResult = llmClient.callAndDecodeResult(splitPath, splitParams, "POST");
 System.out.println("split result: " + openSearchResult.getResult());
 JsonArray array = JsonParser.parseString(openSearchResult.getResult()).getAsJsonArray();
 // 文档推送外层结构, 可添加对文档操作的结构体. 结构内支持一个或多个文档操作内容.
 ArrayList<Map<String, ?>> documents = new ArrayList<>();
 //假设用户原始文档主键为1
 String doc_raw_id="001";
 for(JsonElement element:array){
 JsonObject object = element.getAsJsonObject();
 // 添加文档
 Map<String, Object> add2Document = new HashMap<>();
 Map<String, Object> add2DocumentFields = new HashMap<>();
 // 插入文档内容信息, keyValue 成对匹配.
 // field_pk 字段需与 pkField 字段配置一致.
 add2DocumentFields.put("doc_id", doc_raw_id+"_"+object.get("chunk_id").getAsString());
 add2DocumentFields.put("doc_raw_id", doc_raw_id);
 List<Float> vectors = new ArrayList();
 for(String str: object.get("embedding").getAsString().split(",")){
 vectors.add(Float.parseFloat(str));
 }

 add2DocumentFields.put("split_content_embedding", vectors);
 add2DocumentFields.put("split_content", object.get("chunk"));
 add2DocumentFields.put("time", System.currentTimeMillis());
 // 将文档内容添如 add2Document 结构.
 add2Document.put("fields", add2DocumentFields);
 // 新增对应的文档命令: add
 add2Document.put("cmd", "add");
 documents.add(add2Document);
 }
 System.out.println("push docs: "+documents.toString());


 // 推送数据到向量检索版
 PushDocumentsRequest request = new PushDocumentsRequest();
 request.setBody(documents);
 PushDocumentsResponse response = embeddingClient.pushDocuments(embeddingTableName, embeddingPkField, request);
 String responseBody = response.getBody();
 System.out.println("push result:" + responseBody);


 //对用户原始query进行向量化
 Map<String, String> embeddingParams = new HashMap<String, String>() {
 {
 put("format", "full_json");
 put("_POST_BODY", "{\"content\":\"OpenSearch是什么\",\"query\":true}");
 }};
 String embeddingPath = String.format("/apps/%s/actions/knowledge-embedding", llmAppName);
 openSearchResult = llmClient.callAndDecodeResult(embeddingPath, embeddingParams, "POST");
 System.out.println("query embedding:"+openSearchResult.getResult());
 String embedding = openSearchResult.getResult();

 List<Float> vectors = new ArrayList();
 for(String str: embedding.split(",")){
 vectors.add(Float.parseFloat(str));
 }
 System.out.println("query vectors size:"+vectors.size()+" vectors:"+vectors);
 QueryRequest queryRequest = new QueryRequest();
 queryRequest.setTableName(embeddingIndexName);
 queryRequest.setVector(vectors);
 queryRequest.setTopK(5);
 queryRequest.setIncludeVector(true);
 queryRequest.setOutputFields(Arrays.asList("split_content"));

 FetchRequest fetchRequest = new FetchRequest();
 fetchRequest.setTableName(embeddingIndexName);
 fetchRequest.setIds(Arrays.asList("001_1"));


 SearchResponse searchResponse = embeddingClient.query(queryRequest);
 System.out.println("搜索结果:" + searchResponse.getBody());
 JsonObject recallResult = JsonParser.parseString(searchResponse.getBody()).getAsJsonObject();
 long hits = recallResult.get("totalCount").getAsLong();
 List<String> list = new ArrayList<>();
 if(hits <=0){
 System.out.println("未能召回结果");
 return ;
 }else{
 JsonArray items = recallResult.get("result").getAsJsonArray();
 for(JsonElement element:items) {
 JsonObject object = element.getAsJsonObject();
 String splitContent = object.get("fields").getAsJsonObject().get("split_content").getAsString();
 list.add(splitContent);
 }
 }


 //调用智能问答版实例进行问答
 StringBuffer sb =new StringBuffer();
 sb.append("{ \"question\" : \"什么是opensearch\" ,");
 sb.append(" \"type\" : \"text\",");
 sb.append(" \"content\" : [");
 for(String str:list){
 sb.append("\"");
 sb.append(str);
 sb.append("\"");
 sb.append(",");
 }
 sb.deleteCharAt(sb.lastIndexOf(","));
 sb.append("]}");

 Map<String, String> llmParams = new HashMap<String, String>() {{
 put("format", "full_json");
 put("_POST_BODY", sb.toString());
 }};
 System.out.println("llm request params:"+llmParams);
 String llmPath = String.format("/apps/%s/actions/knowledge-llm", llmAppName);
 openSearchResult = llmClient.callAndDecodeResult(llmPath, llmParams, "POST");
 System.out.println("llm result:"+openSearchResult.getResult());

 }

}

控制台输出示例如下:

split result: 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push result:{"code":200,"status":"OK"}
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llm request params:{_POST_BODY={ "question" : "什么是opensearch" , "type" : "text", "content" : ["OpenSearch是基于阿里巴巴自主研发的大规模分布式搜索引擎搭建的一站式商用智能搜索平台,目前为包括淘宝、天猫、菜鸟在内的阿里集团核心搜索业务提供中台服务支持。经过多年的行业搜索经验沉淀、双11大促流量冲击,智能开放搜索OpenSearch打磨出一套高性能、高时效、高可用、强稳定搜索全家桶服务,包括LLM智能问答版、行业算法版、高性能检索版、向量检索版、召回引擎版五类商品版本,以满足各行各业的搜索需求。OpenSearch以平台服务化的形式,将专业搜索技术简单化、低门槛化和低成本化,让搜索不再成为客户的业务瓶颈,以低成本实现产品搜索功能并快速迭代。"]}, format=full_json}
llm result:{"data":[{"answer":"OpenSearch是阿里巴巴自主研发的一站式商用智能搜索平台,基于大规模分布式搜索引擎搭建,目前为包括淘宝、天猫、菜鸟在内的阿里集团核心搜索业务提供中台服务支持。OpenSearch打磨出一套高性能、高时效、高可用、强稳定搜索全家桶服务,包括LLM智能问答版、行业算法版、高性能检索版、向量检索版、召回引擎版五类商品版本,以满足各行各业的搜索需求。OpenSearch以平台服务化的形式,将专业搜索技术简单化、低门槛化和低成本化,让搜索不再成为客户的业务瓶颈,以低成本实现产品搜索功能并快速迭代。","type":"TEXT"}]}
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