[Backport 2.x] Fix bug where document embedding fails to be generated…

… due to document has dot in field name (#1076) * Fix bug where document embedding fails to be generated due to document has dot in field name (#1062) * Fix bug where document embedding fails to be generated due to document has dot in field name Signed-off-by: Yizhe Liu <yizheliu@amazon.com> * Address comments Signed-off-by: Yizhe Liu <yizheliu@amazon.com> --------- Signed-off-by: Yizhe Liu <yizheliu@amazon.com> * Clean up unused validateFieldName() and use existing methods for TextEmbeddingProcessorIT (#1074) Signed-off-by: Yizhe Liu <yizheliu@amazon.com> --------- Signed-off-by: Yizhe Liu <yizheliu@amazon.com>
opensearch-project · Jan 10, 2025 · 19ea370 · 19ea370
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -26,6 +26,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 - Address inconsistent scoring in hybrid query results ([#998](https://github.com/opensearch-project/neural-search/pull/998))
 - Fix bug where ingested document has list of nested objects ([#1040](https://github.com/opensearch-project/neural-search/pull/1040))
 - Fixed document source and score field mismatch in sorted hybrid queries ([#1043](https://github.com/opensearch-project/neural-search/pull/1043))
+- Fix bug where embedding is missing when ingested document has "." in field name, and mismatches fieldMap config ([#1062](https://github.com/opensearch-project/neural-search/pull/1062))
 ### Infrastructure
 - Update batch related tests to use batch_size in processor & refactor BWC version check ([#852](https://github.com/opensearch-project/neural-search/pull/852))
 - Fix CI for JDK upgrade towards 21 ([#835](https://github.com/opensearch-project/neural-search/pull/835))

diff --git a/src/main/java/org/opensearch/neuralsearch/processor/InferenceProcessor.java b/src/main/java/org/opensearch/neuralsearch/processor/InferenceProcessor.java
@@ -137,6 +137,7 @@ public IngestDocument execute(IngestDocument ingestDocument) throws Exception {
     @Override
     public void execute(IngestDocument ingestDocument, BiConsumer<IngestDocument, Exception> handler) {
         try {
+            preprocessIngestDocument(ingestDocument);
             validateEmbeddingFieldsValue(ingestDocument);
             Map<String, Object> processMap = buildMapWithTargetKeys(ingestDocument);
             List<String> inferenceList = createInferenceList(processMap);
@@ -150,6 +151,15 @@ public void execute(IngestDocument ingestDocument, BiConsumer<IngestDocument, Ex
         }
     }
 
+    @VisibleForTesting
+    void preprocessIngestDocument(IngestDocument ingestDocument) {
+        if (ingestDocument == null || ingestDocument.getSourceAndMetadata() == null) return;
+        Map<String, Object> sourceAndMetadataMap = ingestDocument.getSourceAndMetadata();
+        Map<String, Object> unflattened = ProcessorDocumentUtils.unflattenJson(sourceAndMetadataMap);
+        unflattened.forEach(ingestDocument::setFieldValue);
+        sourceAndMetadataMap.keySet().removeIf(key -> key.contains("."));
+    }
+
     /**
      * This is the function which does actual inference work for batchExecute interface.
      * @param inferenceList a list of String for inference.
@@ -244,12 +254,14 @@ private List<DataForInference> getDataForInference(List<IngestDocumentWrapper> i
         for (IngestDocumentWrapper ingestDocumentWrapper : ingestDocumentWrappers) {
             Map<String, Object> processMap = null;
             List<String> inferenceList = null;
+            IngestDocument ingestDocument = ingestDocumentWrapper.getIngestDocument();
             try {
-                validateEmbeddingFieldsValue(ingestDocumentWrapper.getIngestDocument());
-                processMap = buildMapWithTargetKeys(ingestDocumentWrapper.getIngestDocument());
+                preprocessIngestDocument(ingestDocument);
+                validateEmbeddingFieldsValue(ingestDocument);
+                processMap = buildMapWithTargetKeys(ingestDocument);
                 inferenceList = createInferenceList(processMap);
             } catch (Exception e) {
-                ingestDocumentWrapper.update(ingestDocumentWrapper.getIngestDocument(), e);
+                ingestDocumentWrapper.update(ingestDocument, e);
             } finally {
                 dataForInferences.add(new DataForInference(ingestDocumentWrapper, processMap, inferenceList));
             }
@@ -333,13 +345,14 @@ void buildNestedMap(String parentKey, Object processorKey, Map<String, Object> s
             } else if (sourceAndMetadataMap.get(parentKey) instanceof List) {
                 for (Map.Entry<String, Object> nestedFieldMapEntry : ((Map<String, Object>) processorKey).entrySet()) {
                     List<Map<String, Object>> list = (List<Map<String, Object>>) sourceAndMetadataMap.get(parentKey);
+                    Pair<String, Object> processedNestedKey = processNestedKey(nestedFieldMapEntry);
                     List<Object> listOfStrings = list.stream().map(x -> {
-                        Object nestedSourceValue = x.get(nestedFieldMapEntry.getKey());
+                        Object nestedSourceValue = x.get(processedNestedKey.getKey());
                         return normalizeSourceValue(nestedSourceValue);
                     }).collect(Collectors.toList());
                     Map<String, Object> map = new LinkedHashMap<>();
-                    map.put(nestedFieldMapEntry.getKey(), listOfStrings);
-                    buildNestedMap(nestedFieldMapEntry.getKey(), nestedFieldMapEntry.getValue(), map, next);
+                    map.put(processedNestedKey.getKey(), listOfStrings);
+                    buildNestedMap(processedNestedKey.getKey(), processedNestedKey.getValue(), map, next);
                 }
             }
             treeRes.merge(parentKey, next, REMAPPING_FUNCTION);
@@ -387,7 +400,7 @@ private void validateEmbeddingFieldsValue(IngestDocument ingestDocument) {
         ProcessorDocumentUtils.validateMapTypeValue(
             FIELD_MAP_FIELD,
             sourceAndMetadataMap,
-            fieldMap,
+            ProcessorDocumentUtils.unflattenJson(fieldMap),
             indexName,
             clusterService,
             environment,

diff --git a/src/main/java/org/opensearch/neuralsearch/util/ProcessorDocumentUtils.java b/src/main/java/org/opensearch/neuralsearch/util/ProcessorDocumentUtils.java
@@ -12,11 +12,14 @@
 import org.opensearch.env.Environment;
 import org.opensearch.index.mapper.MapperService;
 
+import java.util.ArrayList;
+import java.util.HashMap;
 import java.util.List;
 import java.util.Locale;
 import java.util.Map;
 import java.util.Objects;
 import java.util.Optional;
+import java.util.Stack;
 
 /**
  * This class is used to accommodate the common code pieces of parsing, validating and processing the document for multiple
@@ -178,4 +181,142 @@ private static void validateDepth(
             );
         }
     }
+
+    /**
+     * Unflatten a JSON object represented as a {@code Map<String, Object>}, possibly with dot in field name,
+     * into a nested {@code Map<String, Object>}
+     * "Object" can be either a {@code Map<String, Object>} or a {@code List<Object>} or simply a String.
+     * For example, input is {"a.b": "c"}, output is {"a":{"b": "c"}}.
+     * Another example:
+     *     input is {"a": [{"b.c": "d"}, {"b.c": "e"}]},
+     *     output is {"a": [{"b": {"c": "d"}}, {"b": {"c": "e"}}]}
+     * @param originalJsonMap the original JSON object represented as a {@code Map<String, Object>}
+     * @return the nested JSON object represented as a nested {@code Map<String, Object>}
+     * @throws IllegalArgumentException  if the originalJsonMap is null or has invalid dot usage in field name
+     */
+    public static Map<String, Object> unflattenJson(Map<String, Object> originalJsonMap) {
+        if (originalJsonMap == null) {
+            throw new IllegalArgumentException("originalJsonMap cannot be null");
+        }
+        Map<String, Object> result = new HashMap<>();
+        Stack<ProcessJsonObjectItem> stack = new Stack<>();
+
+        // Push initial items to stack
+        for (Map.Entry<String, Object> entry : originalJsonMap.entrySet()) {
+            stack.push(new ProcessJsonObjectItem(entry.getKey(), entry.getValue(), result));
+        }
+
+        // Process items until stack is empty
+        while (!stack.isEmpty()) {
+            ProcessJsonObjectItem item = stack.pop();
+            String key = item.key;
+            Object value = item.value;
+            Map<String, Object> currentMap = item.targetMap;
+
+            // Handle nested value
+            if (value instanceof Map) {
+                Map<String, Object> nestedMap = new HashMap<>();
+                for (Map.Entry<String, Object> entry : ((Map<String, Object>) value).entrySet()) {
+                    stack.push(new ProcessJsonObjectItem(entry.getKey(), entry.getValue(), nestedMap));
+                }
+                value = nestedMap;
+            } else if (value instanceof List) {
+                value = handleList((List<Object>) value);
+            }
+
+            // If key contains dot, split and create nested structure
+            unflattenSingleItem(key, value, currentMap);
+        }
+
+        return result;
+    }
+
+    private static List<Object> handleList(List<Object> list) {
+        List<Object> result = new ArrayList<>();
+        Stack<ProcessJsonListItem> stack = new Stack<>();
+
+        // Push initial items to stack
+        for (int i = list.size() - 1; i >= 0; i--) {
+            stack.push(new ProcessJsonListItem(list.get(i), result));
+        }
+
+        // Process items until stack is empty
+        while (!stack.isEmpty()) {
+            ProcessJsonListItem item = stack.pop();
+            Object value = item.value;
+            List<Object> targetList = item.targetList;
+
+            if (value instanceof Map) {
+                Map<String, Object> nestedMap = new HashMap<>();
+                Map<String, Object> sourceMap = (Map<String, Object>) value;
+                for (Map.Entry<String, Object> entry : sourceMap.entrySet()) {
+                    stack.push(new ProcessJsonListItem(new ProcessJsonObjectItem(entry.getKey(), entry.getValue(), nestedMap), targetList));
+                }
+                targetList.add(nestedMap);
+            } else if (value instanceof List) {
+                List<Object> nestedList = new ArrayList<>();
+                for (Object listItem : (List<Object>) value) {
+                    stack.push(new ProcessJsonListItem(listItem, nestedList));
+                }
+                targetList.add(nestedList);
+            } else if (value instanceof ProcessJsonObjectItem) {
+                ProcessJsonObjectItem processJsonObjectItem = (ProcessJsonObjectItem) value;
+                Map<String, Object> tempMap = new HashMap<>();
+                unflattenSingleItem(processJsonObjectItem.key, processJsonObjectItem.value, tempMap);
+                targetList.set(targetList.size() - 1, tempMap);
+            } else {
+                targetList.add(value);
+            }
+        }
+
+        return result;
+    }
+
+    private static void unflattenSingleItem(String key, Object value, Map<String, Object> result) {
+        if (StringUtils.isBlank(key)) {
+            throw new IllegalArgumentException("Field name cannot be null or empty");
+        }
+        if (key.contains(".")) {
+            // Use split with -1 limit to preserve trailing empty strings
+            String[] parts = key.split("\\.", -1);
+            Map<String, Object> current = result;
+
+            for (int i = 0; i < parts.length; i++) {
+                if (StringUtils.isBlank(parts[i])) {
+                    throw new IllegalArgumentException(String.format(Locale.ROOT, "Field name '%s' contains invalid dot usage", key));
+                }
+                if (i == parts.length - 1) {
+                    current.put(parts[i], value);
+                    continue;
+                }
+                current = (Map<String, Object>) current.computeIfAbsent(parts[i], k -> new HashMap<>());
+            }
+        } else {
+            result.put(key, value);
+        }
+    }
+
+    // Helper classes to maintain state during iteration
+    private static class ProcessJsonObjectItem {
+        String key;
+        Object value;
+        Map<String, Object> targetMap;
+
+        ProcessJsonObjectItem(String key, Object value, Map<String, Object> targetMap) {
+            this.key = key;
+            this.value = value;
+            this.targetMap = targetMap;
+        }
+    }
+
+    private static class ProcessJsonListItem {
+        Object value;
+        List<Object> targetList;
+
+        ProcessJsonListItem(Object value, List<Object> targetList) {
+            this.value = value;
+            this.targetList = targetList;
+        }
+    }
+
 }
diff --git a/src/test/java/org/opensearch/neuralsearch/processor/TextEmbeddingProcessorIT.java b/src/test/java/org/opensearch/neuralsearch/processor/TextEmbeddingProcessorIT.java
@@ -50,6 +50,7 @@ public class TextEmbeddingProcessorIT extends BaseNeuralSearchIT {
     private final String INGEST_DOC2 = Files.readString(Path.of(classLoader.getResource("processor/ingest_doc2.json").toURI()));
     private final String INGEST_DOC3 = Files.readString(Path.of(classLoader.getResource("processor/ingest_doc3.json").toURI()));
     private final String INGEST_DOC4 = Files.readString(Path.of(classLoader.getResource("processor/ingest_doc4.json").toURI()));
+    private final String INGEST_DOC5 = Files.readString(Path.of(classLoader.getResource("processor/ingest_doc5.json").toURI()));
     private final String BULK_ITEM_TEMPLATE = Files.readString(
         Path.of(classLoader.getResource("processor/bulk_item_template.json").toURI())
     );
@@ -168,6 +169,23 @@ private void assertDoc(Map<String, Object> sourceMap, String textFieldValue, Opt
         }
     }
 
+    private void assertDocWithLevel2AsList(Map<String, Object> sourceMap) {
+        assertNotNull(sourceMap);
+        assertTrue(sourceMap.containsKey(LEVEL_1_FIELD));
+        assertTrue(sourceMap.get(LEVEL_1_FIELD) instanceof List);
+        List<Map<String, Object>> nestedPassages = (List<Map<String, Object>>) sourceMap.get(LEVEL_1_FIELD);
+        nestedPassages.forEach(nestedPassage -> {
+            assertTrue(nestedPassage.containsKey(LEVEL_2_FIELD));
+            Map<String, Object> level2 = (Map<String, Object>) nestedPassage.get(LEVEL_2_FIELD);
+            Map<String, Object> level3 = (Map<String, Object>) level2.get(LEVEL_3_FIELD_CONTAINER);
+            List<Double> embeddings = (List<Double>) level3.get(LEVEL_3_FIELD_EMBEDDING);
+            assertEquals(768, embeddings.size());
+            for (Double embedding : embeddings) {
+                assertTrue(embedding >= 0.0 && embedding <= 1.0);
+            }
+        });
+    }
+
     public void testTextEmbeddingProcessor_withBatchSizeInProcessor() throws Exception {
         String modelId = null;
         try {
@@ -232,6 +250,44 @@ public void testTextEmbeddingProcessor_withFailureAndSkip() throws Exception {
         }
     }
 
+    @SuppressWarnings("unchecked")
+    public void testNestedFieldMapping_whenDocumentInListIngested_thenSuccessful() throws Exception {
+        String modelId = null;
+        try {
+            modelId = uploadTextEmbeddingModel();
+            loadModel(modelId);
+            createPipelineProcessor(modelId, PIPELINE_NAME, ProcessorType.TEXT_EMBEDDING_WITH_NESTED_FIELDS_MAPPING);
+            createIndexWithPipeline(INDEX_NAME, "IndexMappings.json", PIPELINE_NAME);
+            ingestDocument(INDEX_NAME, INGEST_DOC5, "5");
+
+            assertDocWithLevel2AsList((Map<String, Object>) getDocById(INDEX_NAME, "5").get("_source"));
+
+            NeuralQueryBuilder neuralQueryBuilderQuery = NeuralQueryBuilder.builder()
+                .fieldName(LEVEL_1_FIELD + "." + LEVEL_2_FIELD + "." + LEVEL_3_FIELD_CONTAINER + "." + LEVEL_3_FIELD_EMBEDDING)
+                .queryText(QUERY_TEXT)
+                .modelId(modelId)
+                .k(10)
+                .build();
+
+            QueryBuilder queryNestedLowerLevel = QueryBuilders.nestedQuery(
+                LEVEL_1_FIELD + "." + LEVEL_2_FIELD,
+                neuralQueryBuilderQuery,
+                ScoreMode.Total
+            );
+            QueryBuilder queryNestedHighLevel = QueryBuilders.nestedQuery(LEVEL_1_FIELD, queryNestedLowerLevel, ScoreMode.Total);
+
+            Map<String, Object> searchResponseAsMap = search(INDEX_NAME, queryNestedHighLevel, 2);
+            assertNotNull(searchResponseAsMap);
+
+            assertEquals(1, getHitCount(searchResponseAsMap));
+
+            Map<String, Object> innerHitDetails = getFirstInnerHit(searchResponseAsMap);
+            assertEquals("5", innerHitDetails.get("_id"));
+        } finally {
+            wipeOfTestResources(INDEX_NAME, PIPELINE_NAME, modelId, null);
+        }
+    }
+
     private String uploadTextEmbeddingModel() throws Exception {
         String requestBody = Files.readString(Path.of(classLoader.getResource("processor/UploadModelRequestBody.json").toURI()));
         return registerModelGroupAndUploadModel(requestBody);