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Adds Json Parsing to nested object during update Query step in ML Inference Request processor (#3856) (#3867)
* adds json parsing to modelOutputValue during query rewrite * add Unit Test for queryTemplate change * refactors and adds new UT * apply spotless * replace tab with spaces on multi-line comment --------- (cherry picked from commit 532284e) Signed-off-by: Brian Flores <iflorbri@amazon.com> Co-authored-by: Brian Flores <iflorbri@amazon.com>
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‎plugin/src/main/java/org/opensearch/ml/processor/MLInferenceSearchRequestProcessor.java

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@@ -360,6 +360,9 @@ private String updateQueryTemplate(String queryTemplate, Map<String, String> out
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String newQueryField = outputMapEntry.getKey();
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String modelOutputFieldName = outputMapEntry.getValue();
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Object modelOutputValue = getModelOutputValue(mlOutput, modelOutputFieldName, ignoreMissing, fullResponsePath);
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if (modelOutputValue instanceof Map) {
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modelOutputValue = toJson(modelOutputValue);
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}
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valuesMap.put(newQueryField, modelOutputValue);
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}
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StringSubstitutor sub = new StringSubstitutor(valuesMap);

‎plugin/src/test/java/org/opensearch/ml/processor/MLInferenceSearchRequestProcessorTests.java

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@@ -41,10 +41,13 @@
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import org.opensearch.core.action.ActionListener;
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import org.opensearch.core.xcontent.NamedXContentRegistry;
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import org.opensearch.core.xcontent.XContentParser;
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import org.opensearch.index.query.MatchAllQueryBuilder;
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import org.opensearch.index.query.QueryBuilder;
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import org.opensearch.index.query.QueryBuilders;
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import org.opensearch.index.query.RangeQueryBuilder;
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import org.opensearch.index.query.TermQueryBuilder;
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import org.opensearch.index.query.TermsQueryBuilder;
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import org.opensearch.index.query.functionscore.ScriptScoreQueryBuilder;
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import org.opensearch.ingest.Processor;
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import org.opensearch.ml.common.dataset.remote.RemoteInferenceInputDataSet;
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import org.opensearch.ml.common.input.MLInput;
@@ -58,6 +61,8 @@
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import org.opensearch.ml.searchext.MLInferenceRequestParameters;
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import org.opensearch.ml.searchext.MLInferenceRequestParametersExtBuilder;
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import org.opensearch.plugins.SearchPlugin;
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import org.opensearch.script.Script;
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import org.opensearch.script.ScriptType;
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import org.opensearch.search.SearchModule;
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import org.opensearch.search.builder.SearchSourceBuilder;
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import org.opensearch.search.pipeline.PipelineProcessingContext;
@@ -1312,7 +1317,7 @@ public void onFailure(Exception e) {
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/**
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* Tests the successful rewriting of a complex nested array in query extension based on the model output.
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* verify the pipelineConext is set from the extension
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* verify the pipelineContext is set from the extension
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* @throws Exception if an error occurs during the test
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*/
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public void testExecute_rewriteTermQueryReadAndWriteComplexNestedArrayToExtensionSuccess() throws Exception {
@@ -1499,6 +1504,207 @@ public void onFailure(Exception e) {
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}
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/**
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* Tests ML Processor can return a sparse vector correctly when performing a rewrite query.
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*
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* This simulates a real world scenario where user has a neural sparse model and attempts to parse
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* it by asserting FullResponsePath to true.
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* @throws Exception when an error occurs on the test
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*/
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public void testExecute_rewriteTermQueryWithSparseVectorSuccess() throws Exception {
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String modelInputField = "inputs";
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String originalQueryField = "query.term.text.value";
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String newQueryField = "vector";
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String modelInferenceJsonPathInput = "$.inference_results[0].output[0].dataAsMap.response[0]";
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String queryTemplate = "{\n"
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+ " \"query\": {\n"
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+ " \"script_score\": {\n"
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+ " \"query\": {\n"
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+ " \"match_all\": {}\n"
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+ " },\n"
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+ " \"script\": {\n"
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+ " \"source\": \"return 1;\",\n"
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+ " \"params\": {\n"
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+ " \"query_tokens\": ${vector}\n"
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+ " }\n"
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+ " }\n"
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+ " }\n"
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+ " }\n"
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+ "}";
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Map<String, Double> sparseVector = Map.of("this", 1.3123, "which", 0.2447, "here", 0.6674);
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List<Map<String, String>> optionalInputMap = new ArrayList<>();
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Map<String, String> input = new HashMap<>();
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input.put(modelInputField, originalQueryField);
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optionalInputMap.add(input);
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List<Map<String, String>> optionalOutputMap = new ArrayList<>();
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Map<String, String> output = new HashMap<>();
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output.put(newQueryField, modelInferenceJsonPathInput);
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optionalOutputMap.add(output);
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MLInferenceSearchRequestProcessor requestProcessor = new MLInferenceSearchRequestProcessor(
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"model1",
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queryTemplate,
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null,
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null,
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optionalInputMap,
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optionalOutputMap,
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null,
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DEFAULT_MAX_PREDICTION_TASKS,
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PROCESSOR_TAG,
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DESCRIPTION,
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false,
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"remote",
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true,
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false,
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"{ \"parameters\": ${ml_inference.parameters} }",
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client,
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TEST_XCONTENT_REGISTRY_FOR_QUERY
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);
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/**
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* {
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* "inference_results" : [ {
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* "output" : [ {
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* "name" : "response",
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* "dataAsMap" : {
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* "response" : [ {
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* "this" : 1.3123,
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* "which" : 0.2447,
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* "here" : 0.6674
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* } ]
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* }
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* } ]
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* } ]
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* }
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*/
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ModelTensor modelTensor = ModelTensor.builder().name("response").dataAsMap(Map.of("response", List.of(sparseVector))).build();
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ModelTensors modelTensors = ModelTensors.builder().mlModelTensors(Arrays.asList(modelTensor)).build();
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ModelTensorOutput mlModelTensorOutput = ModelTensorOutput.builder().mlModelOutputs(Arrays.asList(modelTensors)).build();
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1588+
doAnswer(invocation -> {
1589+
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
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actionListener.onResponse(MLTaskResponse.builder().output(mlModelTensorOutput).build());
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return null;
1592+
}).when(client).execute(any(), any(), any());
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QueryBuilder incomingQuery = new TermQueryBuilder("text", "foo");
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SearchSourceBuilder source = new SearchSourceBuilder().query(incomingQuery);
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SearchRequest request = new SearchRequest().source(source);
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ActionListener<SearchRequest> Listener = new ActionListener<>() {
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@Override
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public void onResponse(SearchRequest newSearchRequest) {
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Script script = new Script(ScriptType.INLINE, "painless", "return 1;", Map.of("query_tokens", sparseVector));
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ScriptScoreQueryBuilder expectedQuery = new ScriptScoreQueryBuilder(QueryBuilders.matchAllQuery(), script);
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assertEquals(expectedQuery, newSearchRequest.source().query());
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}
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@Override
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public void onFailure(Exception e) {
1609+
throw new RuntimeException("Failed in executing processRequestAsync.", e);
1610+
}
1611+
};
1612+
1613+
requestProcessor.processRequestAsync(request, requestContext, Listener);
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}
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/**
1617+
* Tests ML Processor can return a OpenSearch Query correctly when performing a rewrite query.
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*
1619+
* This simulates a real world scenario where user has a llm return a OpenSearch Query to help them generate a new
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* query based on the context given in the prompt.
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*
1622+
* @throws Exception when an error occurs on the test
1623+
*/
1624+
public void testExecute_rewriteTermQueryWithNewQuerySuccess() throws Exception {
1625+
String modelInputField = "inputs";
1626+
String originalQueryField = "query.term.text.value";
1627+
String newQueryField = "llm_query";
1628+
String modelInferenceJsonPathInput = "$.inference_results[0].output[0].dataAsMap.content[0].text";
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1630+
String queryTemplate = "${llm_query}";
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1632+
String llmQuery = "{\n" + " \"query\": {\n" + " \"match_all\": {}\n" + " }\n" + "}";
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Map content = Map.of("content", List.of(Map.of("text", llmQuery)));
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1635+
List<Map<String, String>> optionalInputMap = new ArrayList<>();
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Map<String, String> input = new HashMap<>();
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input.put(modelInputField, originalQueryField);
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optionalInputMap.add(input);
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1640+
List<Map<String, String>> optionalOutputMap = new ArrayList<>();
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Map<String, String> output = new HashMap<>();
1642+
output.put(newQueryField, modelInferenceJsonPathInput);
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optionalOutputMap.add(output);
1644+
1645+
MLInferenceSearchRequestProcessor requestProcessor = new MLInferenceSearchRequestProcessor(
1646+
"model1",
1647+
queryTemplate,
1648+
null,
1649+
null,
1650+
optionalInputMap,
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optionalOutputMap,
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null,
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DEFAULT_MAX_PREDICTION_TASKS,
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PROCESSOR_TAG,
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DESCRIPTION,
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false,
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"remote",
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true,
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false,
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"{ \"parameters\": ${ml_inference.parameters} }",
1661+
client,
1662+
TEST_XCONTENT_REGISTRY_FOR_QUERY
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);
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/*
1666+
* {
1667+
* "inference_results" : [ {
1668+
* "output" : [ {
1669+
* "name" : "response",
1670+
* "dataAsMap" : {
1671+
* "content": [
1672+
* "text": "{\"query\": \"match_all\" : {}}"
1673+
* }
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* } ]
1675+
* } ]
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* }
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*/
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ModelTensor modelTensor = ModelTensor.builder().name("response").dataAsMap(content).build();
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ModelTensors modelTensors = ModelTensors.builder().mlModelTensors(Arrays.asList(modelTensor)).build();
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ModelTensorOutput mlModelTensorOutput = ModelTensorOutput.builder().mlModelOutputs(Arrays.asList(modelTensors)).build();
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doAnswer(invocation -> {
1683+
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
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actionListener.onResponse(MLTaskResponse.builder().output(mlModelTensorOutput).build());
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return null;
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}).when(client).execute(any(), any(), any());
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QueryBuilder incomingQuery = new TermQueryBuilder("text", "foo");
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SearchSourceBuilder source = new SearchSourceBuilder().query(incomingQuery);
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SearchRequest sampleRequest = new SearchRequest().source(source);
1691+
1692+
ActionListener<SearchRequest> Listener = new ActionListener<>() {
1693+
@Override
1694+
public void onResponse(SearchRequest newSearchRequest) {
1695+
MatchAllQueryBuilder expectedQuery = new MatchAllQueryBuilder();
1696+
assertEquals(expectedQuery, newSearchRequest.source().query());
1697+
}
1698+
1699+
@Override
1700+
public void onFailure(Exception e) {
1701+
throw new RuntimeException("Failed in executing processRequestAsync.", e);
1702+
}
1703+
};
1704+
1705+
requestProcessor.processRequestAsync(sampleRequest, requestContext, Listener);
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}
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15021708
/**
15031709
* Tests when there are two optional input fields
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* but only the second optional input is present in the query

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