Implement RAG for OpenSearch Vector Search Edition - OpenSearch

This topic describes how to provide retrieval-augmented generation (RAG) capabilities for an OpenSearch Vector Search Edition instance by using an OpenSearch LLM-Based Conversational Search Edition instance.

If you already have an OpenSearch Vector Search Edition instance, you need to only purchase an OpenSearch LLM-Based Conversational Search Edition instance. The two instances provide the following functionalities:

OpenSearch Vector Search Edition instance:

Stores original document data and vector data.
Retrieves original document data and vector data.

OpenSearch LLM-Based Conversational Search Edition instance:

Optional. Performs text segmentation and word embedding on original documents.
Optional. Performs word embedding on original queries.
Performs inference and summarization on the retrieved results.

The following figure shows the implementation process.

1. Create and configure instances

1.1. Create and configure an OpenSearch Vector Search Edition instance

1.1.1. Purchase an OpenSearch Vector Search Edition instance

If you already have an OpenSearch Vector Search Edition instance, you do not need to purchase a new one.

For more information about how to purchase an OpenSearch Vector Search Edition instance, see Purchase an OpenSearch Vector Search Edition instance.

1.1.2. Configure the OpenSearch Vector Search Edition instance

If you do not want to modify the query and retrieval logic of an existing OpenSearch Vector Search Edition instance, you need to only call the knowledge-llm operation of LLM-Based Conversational Search Edition to perform inference and summarization on the retrieved results. For more information, see the "Perform inference and summarization by calling the knowledge-llm operation" section of this topic.

A newly purchased instance is in the Pending Configuration state, and you need to configure a table for the instance. To do this, click Configure in the Actions column of the instance, as shown in the following figure.

1.1.2.1. Configure the basic information of a table

Parameters:

Table Name: the name of the table. You can customize the table name.

Data Shards: the number of data shards in the table. If you create multiple index tables in the instance, make sure that the index tables contain the same number of shards. Alternatively, make sure that at least one index table contains one shard and other index tables contain the same number of shards.

Number of Resources for Data Updates: the number of resources used for data updates. By default, OpenSearch provides a free quota of two resources for data updates for each data source in an OpenSearch Vector Search Edition instance. Each resource consists of 4 CPU cores and 8 GB of memory. You are charged for resources that exceed the free quota. For more information, see Billing overview of OpenSearch Vector Search Edition.

Scenario Template: the template that is used to create the table. In this example, Common Template is selected.

Click Next to go to the Data Synchronization step to add a data source.

1.1.2.2. Add a data source

Parameters:

Full Data Source: the type of the data source. In this example, API is selected, which indicates that the user data is pushed to the OpenSearch Vector Search Edition instance by using an API.

Click Next to go to the Field Configuration step to configure fields.

1.1.2.3. Configure fields

The table schema must contain the following fields:

The primary key field of documents, such as the doc_id field in the preceding figure.
The field that stores the content obtained after text segmentation, such as the split_content field in the preceding figure.
The field that stores the vectors of the content obtained after word embedding, such as the split_content_embedding field in the preceding figure. You must define this field as a vector field and separate multiple values of the vector field with commas (,).

In addition to the preceding required fields, you can add custom fields and use these fields for sorting and filtering based on your business requirements.

1.1.2.4. Configure the index schema

Parameters:

Vector Dimension: the number of vector dimensions. In this example, set the parameter to 1536.

Use the default settings for other parameters.

1.1.2.5. Confirm the creation

In the Confirm step, click Confirm.

You can click Change History in the left-side navigation pane to view the table creation progress. After the progress is complete, the table is created and you can perform subsequent operations.

1.2. Create and configure an OpenSearch LLM-Based Conversational Search Edition instance

1.2.1. Purchase and configure an OpenSearch LLM-Based Conversational Search Edition instance

For more information about how to purchase an OpenSearch LLM-Based Conversational Search Edition instance, see the "Purchase an instance" section of the Implement enterprise-specific conversational search topic.

After you purchase an OpenSearch LLM-Based Conversational Search Edition instance, you can use the instance without the need to configure the instance.

2. Push data to the OpenSearch Vector Search Edition instance

2.1. Perform text segmentation and word embedding on original documents

The word embedding model supports a maximum of 300 tokens. However, the original documents are usually large in size. Therefore, you need to perform text segmentation on the original documents. In addition, you need to perform word embedding on the documents after text segmentation is performed.

For more information about the endpoint and API of the OpenSearch LLM-Based Conversational Search Edition instance, see the "Sample code of the SDK for Java" section of this topic or the following topic:

SplitDoc

2.2. Push the results obtained after text segmentation and word embedding to the OpenSearch Vector Search Edition instance

In the returned results of Step 2.1:

The chunk_id field stores the ID of the chunk obtained after text segmentation. Concatenate the chunk ID and the ID of the original document to generate the value of the primary key field for the chunk. This primary key field is doc_id.
The chunk field stores the chunk content obtained after text segmentation, which must be pushed to the split_content field.
The embedding field stores the vector of the chunk content after word embedding, which must be pushed to the split_content_embedding field.

Push the preceding results to the OpenSearch Vector Search Edition instance.

For more information about the endpoint and API of the OpenSearch Vector Search Edition instance, see the "Sample code of the SDK for Java" section of this topic or the following topic:

Data push demo.

3. Perform a conversational search

3.1. Perform word embedding on the original query

You need to perform word embedding on the original query.

EmbeddingDoc

3.2. Retrieve the vector results

Use the embedding of the original query to search in the OpenSearch Vector Search Edition instance. In general, you need to retrieve only the top 5 results.

For more information about the endpoint and API of the OpenSearch Vector Search Edition instance, see the "Sample code of the SDK for Java" section of this topic or the following topic:

Document search demo

3.3. Perform inference and summarization by calling the knowledge-llm operation

Perform inference and summarization on the results retrieved from the OpenSearch Vector Search Edition instance by calling the knowledge-llm operation provided by OpenSearch LLM-Based Conversational Search Edition.

For more information, see the "Sample code of the SDK for Java" section of this topic or the following topic

knowledge-llm

4. Sample code of the SDK for Java

The following sample code provides an example on how to use the SDK for Java to push data and perform conversational searches.

Maven dependencies:

<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>

Sample code of the SDK for Java:

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 {

 /**
 * The name of the OpenSearch LLM-Based Conversational Search Edition instance.
 */
 private static String llmAppName = "test_llm";
 /**
 * The endpoint of the OpenSearch LLM-Based Conversational Search Edition instance.
 */
 private static String llmHost = "http://opensearch-cn-shanghai.aliyuncs.com";
 /**
 * The AccessKey ID that is used to access the OpenSearch LLM-Based Conversational Search Edition instance.
 */
 private static String llmAccessKey = "xxxxx";
 /**
 * The AccessKey secret that is used to access the OpenSearch LLM-Based Conversational Search Edition instance.
 */
 private static String llmAccessSecret = "xxxx";



 /**
 * The API endpoint of the OpenSearch Vector Search Edition instance.
 */
 private static String embeddingEndpoint = "ha-cn-xxxx.public.ha.aliyuncs.com";
 /**
 * The name of the OpenSearch Vector Search Edition instance.
 */
 private static String embeddingInstanceId = "ha-cn-xxx";
 /**
 * The name of the document data table in the OpenSearch Vector Search Edition instance.
 */
 private static String embeddingTableName = "ha-cn-xxx_xxx";


 /**
 * The name of the document index table in the OpenSearch Vector Search Edition instance.
 */
 private static String embeddingIndexName = "xxx";
 /**
 * The primary key field of the documents pushed to the OpenSearch Vector Search Edition instance.
 */
 private static String embeddingPkField = "doc_id";
 /**
 * The username that is used to access the OpenSearch Vector Search Edition instance.
 */
 private static String embeddingUserName = "xxxx";
 /**
 * The password that is used to access the OpenSearch Vector Search Edition instance.
 */
 private static String embeddingPassword = "xxxx";



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

 // Create an object to access the OpenSearch LLM-Based Conversational Search Edition instance.
 // Create an OpenSearch object.
 OpenSearch openSearch = new OpenSearch(llmAccessKey, llmAccessSecret, llmHost);
 // Use the OpenSearch object as a parameter to create an OpenSearchClient object.
 OpenSearchClient llmClient = new OpenSearchClient(openSearch);

 // Create an object to access the OpenSearch Vector Search Edition instance.
 Config config = new Config();
 config.setEndpoint(embeddingEndpoint);
 config.setInstanceId(embeddingInstanceId);
 config.setAccessUserName(embeddingUserName);
 config.setAccessPassWord(embeddingPassword);
 Client embeddingClient = new Client(config);



 // Perform text segmentation and word embedding on the content.
 Map<String, String> splitParams = new HashMap<String, String>() {{
 put("format", "full_json");
 put("_POST_BODY", "{\"content\":\"OpenSearch serves as an all-in-one platform used to develop commercial intelligent search services. It is built based on a large-scale distributed search engine platform developed by Alibaba. OpenSearch provides mid-end services for the core search business of Alibaba Group, including Taobao, Tmall, and Cainiao." +
 "Thanks to years of search experience in various industries and the capabilities of handling traffic peaks during massive online promotions, the team of OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions are LLM-Based Conversational Search Edition, Industry Algorithm Edition, High-performance Search Edition, Vector Search Edition, and Retrieval Engine Edition. This helps meet search requirements in a variety of industries." +
 "OpenSearch is a PaaS that simplifies the use of search technologies and lowers the technical threshold and costs. OpenSearch allows you to implement search features and fast iterations for your services at a low cost. This prevents search technologies from being your business bottleneck.\",\"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();
 // The outer structure that specifies document operations. You can specify one or more document operations in the structure.
 ArrayList<Map<String, ?>> documents = new ArrayList<>();
 // For example, the primary key value of the original document is 001.
 String doc_raw_id="001";
 for(JsonElement element:array){
 JsonObject object = element.getAsJsonObject();
 // Upload the document.
 Map<String, Object> add2Document = new HashMap<>();
 Map<String, Object> add2DocumentFields = new HashMap<>();
 // Insert the content of the document. Keys must be paired with values.
 // The value of the field_pk field must be the same as the value of the pkField field.
 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());
 // Add the document content to an add2Document structure.
 add2Document.put("fields", add2DocumentFields);
 // Run the add command to upload the document.
 add2Document.put("cmd", "add");
 documents.add(add2Document);
 }
 System.out.println("push docs:"+documents.toString());


 // Push the data to the OpenSearch Vector Search Edition instance.
 PushDocumentsRequest request = new PushDocumentsRequest();
 request.setBody(documents);
 PushDocumentsResponse response = embeddingClient.pushDocuments(embeddingTableName, embeddingPkField, request);
 String responseBody = response.getBody();
 System.out.println("push result:" + responseBody);


 // Perform word embedding on the original query.
 Map<String, String> embeddingParams = new HashMap<String, String>() {
 {
 put("format", "full_json");
 put("_POST_BODY", "{\"content\":\"What is OpenSearch\",\"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("Search results:" + 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("No results found.");
 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);
 }
 }


 // Perform a conversational search by using the OpenSearch LLM-Based Conversational Search Edition instance.
 StringBuffer sb =new StringBuffer();
 sb.append("{ \"question\" : \"What is 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());

 }

}

Sample result of a conversational search:

split result:[{"chunk":"OpenSearch serves as an all-in-one platform used to develop commercial intelligent search services. It is built based on a large-scale distributed search engine platform developed by Alibaba. OpenSearch provides mid-end services for the core search business of Alibaba Group, including Taobao, Tmall, and Cainiao. Thanks to years of search experience in various industries and the capabilities of handling traffic peaks during massive online promotions, the team of OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions are LLM-Based Conversational Search Edition, Industry Algorithm Edition, High-performance Search Edition, Vector Search Edition, and Retrieval Engine Edition. This helps meet search requirements in a variety of industries. OpenSearch is a PaaS that simplifies the use of search technologies and lowers the technical threshold and costs. OpenSearch allows you to implement search features and fast iterations for your services at a low cost. This prevents search technologies from being your business bottleneck. 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It is built based on a large-scale distributed search engine platform developed by Alibaba. OpenSearch provides mid-end services for the core search business of Alibaba Group, including Taobao, Tmall, and Cainiao. Thanks to years of search experience in various industries and the capabilities of handling traffic peaks during massive online promotions, the team of OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions are LLM-Based Conversational Search Edition, Industry Algorithm Edition, High-performance Search Edition, Vector Search Edition, and Retrieval Engine Edition. This helps meet search requirements in a variety of industries. OpenSearch is a PaaS that simplifies the use of search technologies and lowers the technical threshold and costs. OpenSearch allows you to implement search features and fast iterations for your services at a low cost. This prevents search technologies from being your business bottleneck.", doc_id=001_1, doc_raw_id=001}}]
push result:{"code":200,"status":"OK"}
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serves as an all-in-one platform used to develop commercial intelligent search services. It is built based on a large-scale distributed search engine platform developed by Alibaba. OpenSearch provides mid-end services for the core search business of Alibaba Group, including Taobao, Tmall, and Cainiao. Thanks to years of search experience in various industries and the capabilities of handling traffic peaks during massive online promotions, the team of OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions are LLM-Based Conversational Search Edition, Industry Algorithm Edition, High-performance Search Edition, Vector Search Edition, and Retrieval Engine Edition. This helps meet search requirements in a variety of industries. OpenSearch is a PaaS that simplifies the use of search technologies and lowers the technical threshold and costs. OpenSearch allows you to implement search features and fast iterations for your services at a low cost. This prevents search technologies from being your business bottleneck. "}}],"totalTime":5.858}
llm request params:{_POST_BODY={ "question" : "What is OpenSearch" , "type" : "text" , "content" : ["OpenSearch serves as an all-in-one platform used to develop commercial intelligent search services. It is built based on a large-scale distributed search engine platform developed by Alibaba. OpenSearch provides mid-end services for the core search business of Alibaba Group, including Taobao, Tmall, and Cainiao. Thanks to years of search experience in various industries and the capabilities of handling traffic peaks during massive online promotions, the team of OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions are LLM-Based Conversational Search Edition, Industry Algorithm Edition, High-performance Search Edition, Vector Search Edition, and Retrieval Engine Edition. This helps meet search requirements in a variety of industries. OpenSearch is a PaaS that simplifies the use of search technologies and lowers the technical threshold and costs. OpenSearch allows you to implement search features and fast iterations for your services at a low cost. This prevents search technologies from being your business bottleneck."]}, format=full_json}
llm result:{"data":[{"answer":"OpenSearch is an all-in-one platform built based on a large-scale distributed search engine platform developed by Alibaba. It is used to develop commercial intelligent search services. OpenSearch provides mid-end services for the core search business of Alibaba Group, including Taobao, Tmall, and Cainiao. The team of OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions are LLM-Based Conversational Search Edition, Industry Algorithm Edition, High-performance Search Edition, Vector Search Edition, and Retrieval Engine Edition. This helps meet search requirements in a variety of industries. OpenSearch is a PaaS that simplifies the use of search technologies and lowers the technical threshold and costs. OpenSearch allows you to implement search features and fast iterations for your services at a low cost. This prevents search technologies from being your business bottleneck. ","type":"TEXT"}]}