This topic describes how to provide retrieval-augmented generation (RAG) capabilities for an OpenSearch Retrieval Engine Edition instance by using an OpenSearch LLM-Based Conversational Search Edition instance.
Important
This topic is suitable for OpenSearch Retrieval Engine Edition instances of the classic version (instances created before June 2024). For information about how to provide RAG capabilities for an OpenSearch Retrieval Engine Edition instance created after June 2024, see Implement RAG for OpenSearch Retrieval Engine Edition of the new version.
If you already have an OpenSearch Retrieval Engine Edition instance, you need to only purchase an OpenSearch LLM-Based Conversational Search Edition instance. The two instances provide the following functionalities:
OpenSearch Retrieval Engine 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 procedure.
1. Create and configure instances
1.1. Create and configure an OpenSearch Retrieval Engine Edition instance
1.1.1. Purchase an OpenSearch Retrieval Engine Edition instance
If you already have an OpenSearch Retrieval Engine Edition instance, you do not need to purchase a new one.
For more information about how to purchase an OpenSearch Retrieval Engine Edition instance, see Purchase an OpenSearch Retrieval Engine Edition instance.
1.1.2. Configure the OpenSearch Retrieval Engine Edition instance
If you do not want to modify the query and retrieval logic of an existing OpenSearch Retrieval Engine Edition instance, you need to only call the knowledge-llm operation of OpenSearch 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.
If you want to perform text segmentation and word embedding on the data of an existing OpenSearch Retrieval Engine Edition instance, you need to modify the configurations of the instance.
On the Instances page, find the instance that you want to manage and click Configure in the Actions column, as shown in the following figure.
1.1.2.1. Configure a data source
Click Add Data Source. In the panel that appears, select API Data Source as Data Source Type.
Parameter:
Data Source Name: the name of the data source. You can enter a custom name.
Click Verify. In the message that appears, click OK. The data source is displayed in the data source list. Click Next to configure the index schema.
1.1.2.2. Configure the index schema
To store the chunk data and vector data, make sure that your table schema contains the following fields:
The primary key field of a document, such as the doc_id field in the following figure.
The field that stores the chunks obtained after text segmentation, such as the split_content field in the following figure.
The field that stores the vectors of the chunk content obtained after word embedding, such as the split_content_embedding field in the following 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 document search, sorting, and filtering based on your business requirements. The following figure shows an example.
Take note of the following items when you configure the index settings:
Configure a PRIMARYKEY64 index for the doc_id field.
Configure a PACK or TEXT index for the content and split_content fields.
Configure a vector index for the split_content_embedding field. Set the number of vector dimensions to 1536 and use the default values for other parameters.
The following figure shows an example.
After the configurations are complete, click Publish. In the message that appears, click OK. The table schema and indexes on the specified fields are generated.
Click Next to rebuild the indexes.
1.1.2.3. Rebuild the indexes
Select the API data source that you added. Click Enter Current Time.
Click Next. The configurations are complete.
You can click Change History in the left-side navigation pane to view the reindexing progress. After the progress is complete, the table is ready, 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, see Implement enterprise-specific conversational search.
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 Retrieval Engine Edition instance
2.1. Perform text segmentation and word embedding on original documents
If the original documents are large in size, you cannot directly call the knowledge-llm operation of OpenSearch LLM-Based Conversational Search Edition to perform inference and summarization on the retrieved original documents.
In this case, you need to send a request to an endpoint of the OpenSearch LLM-Based Conversational Search Edition instance to perform text segmentation and word embedding on the original documents.
For more information, see the "Sample code of the SDK for Java" section of this topic or the following topic:
Text segmentation and vectorization
2.2. Push the results obtained after text segmentation and word embedding to the OpenSearch Retrieval Engine Edition instance
In the returned results of Step 2.1:
The chunk_id parameter indicates the ID of the chunk 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 parameter indicates the chunk content after text segmentation, which must be pushed to the split_content field.
The embedding parameter indicates 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 Retrieval Engine Edition instance.
For more information about the endpoint and API of the OpenSearch Retrieval Engine Edition instance, see the "Sample code of the SDK for Java" section of this topic or the following topic:
3. Perform conversational searches
3.1. Perform word embedding on original queries
Perform word embedding on original queries.
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:
3.2. Retrieve the vector results
Use the vectors of the original queries to retrieve data in the OpenSearch Retrieval Engine Edition instance.
In addition to vector-based retrieval, you can use other fields to retrieve data and sort and filter the retrieval results.
For more information about the endpoint and API of the OpenSearch Retrieval Engine Edition instance, see the "Sample code of the SDK for Java" section of this topic or the following topic:
3.3. Perform inference and summarization by calling the knowledge-llm operation
Perform inference and summarization on the results retrieved from the OpenSearch Retrieval Engine 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:
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</artifactId>
<version>1.3.6</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.Client;
import com.aliyun.ha3engine.models.*;
import com.aliyun.ha3engine.vector.models.QueryRequest;
import com.aliyun.opensearch.OpenSearchClient;
import com.aliyun.opensearch.sdk.generated.OpenSearch;
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 RetrievalWithLLMDemo {
/**
* The name of the OpenSearch LLM-Based Conversational Search Edition instance.
*/
private static String llmAppName = "xxxx";
/**
* 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 = "xxx";
/**
* The AccessKey secret that is used to access the OpenSearch LLM-Based Conversational Search Edition instance.
*/
private static String llmAccessSecret = "xxx";
/**
* The API endpoint of the OpenSearch Retrieval Engine Edition instance.
*/
private static String retrievalEndpoint = "ha-cn-xxx.public.ha.aliyuncs.com";
/**
* The name of the OpenSearch Retrieval Engine Edition instance.
*/
private static String retrievalInstanceId = "ha-cn-xxx";
/**
* The name of the document data table in the OpenSearch Retrieval Engine Edition instance.
*/
private static String retrievalTableName = "ha-cn-xxx";
/**
* The primary key field of the documents pushed to the OpenSearch Retrieval Engine Edition instance.
*/
private static String retrievalPkField = "doc_id";
/**
* The username that is used to access the OpenSearch Retrieval Engine Edition instance.
*/
private static String retrievalUserName = "xxx";
/**
* The password that is used to access the OpenSearch Retrieval Engine Edition instance.
*/
private static String retrievalPassword = "xxx";
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 Retrieval Engine Edition instance.
Config config = new Config();
config.setEndpoint(retrievalEndpoint);
config.setInstanceId(retrievalInstanceId);
config.setAccessUserName(retrievalUserName);
config.setAccessPassWord(retrievalPassword);
Client retrievalClient = 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 Double 11, OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions include 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"));
// 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 Retrieval Engine Edition instance.
PushDocumentsRequestModel request = new PushDocumentsRequestModel();
request.setBody(documents);
PushDocumentsResponseModel response = retrievalClient.pushDocuments(retrievalTableName, retrievalPkField, 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();
SearchRequestModel haQueryRequestModel = new SearchRequestModel();
SearchQuery haRawQuery = new SearchQuery();
haRawQuery.setQuery("query=split_content_embedding:'"+embedding+"'&&config=start:0,hit:5,format:json&&cluster=general");
haQueryRequestModel.setQuery(haRawQuery);
// The request method. Only the GET and POST methods are supported. Default value: GET. If the length of the query exceeds 30 KB, use the POST method.
haQueryRequestModel.setMethod("POST");
SearchResponseModel searchResponse = retrievalClient.Search(haQueryRequestModel);
System.out.println("Search results:" + searchResponse.getBody());
JsonObject recallResult = JsonParser.parseString(searchResponse.getBody()).getAsJsonObject().get("result").getAsJsonObject();
long hits = recallResult.get("totalHits").getAsLong();
List<String> list = new ArrayList<>();
if(hits <=0){
System.out.println("No results found.");
return ;
}else{
JsonArray items = recallResult.get("items").getAsJsonArray();
for(JsonElement element:items) {
JsonObject object = element.getAsJsonObject();
String splitContent = object.get("fields").getAsJsonObject().get("split_content").getAsString();
list.add(splitContent);
}
}
// Perform inference and summarization on the retrieved results 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 Double 11, OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions include 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 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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 Double 11, OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions include 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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search result:{"result":{"searchtime":0.07891,"numHits":1,"totalHits":1,"coveredPercent":100.0,"items":[{"fields":{"doc_id":"001_1","doc_raw_id":"001","content":"","split_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 Double 11, OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions include 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 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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 Double 11, OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions include 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 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 Double 11, OpenSearch releases a set of service editions that offers high performance, efficiency, availability, and stability. The released service editions include 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. In summary, OpenSearch is a platform used to develop intelligent search services. Opensearch provides various search capabilities to meet search requirements in a variety of industries.","type":"TEXT"}]}