Add RAGTool

ml-algorithms/src/main/java/org/opensearch/ml/engine/tools/AbstractRetrieverTool.java

@@ -42,7 +42,8 @@ public abstract class AbstractRetrieverTool implements Tool {
     public static final String INDEX_FIELD = "index";
     public static final String SOURCE_FIELD = "source_field";
     public static final String DOC_SIZE_FIELD = "doc_size";
-
+    public static final int DEFAULT_DOC_SIZE = 2;
+    public static final int DEFAULT_K = 10;
     protected String description = DEFAULT_DESCRIPTION;
     protected Client client;
     protected NamedXContentRegistry xContentRegistry;
@@ -61,7 +62,7 @@ protected AbstractRetrieverTool(
         this.xContentRegistry = xContentRegistry;
         this.index = index;
         this.sourceFields = sourceFields;
-        this.docSize = docSize == null ? 2 : docSize;
+        this.docSize = docSize == null ? DEFAULT_DOC_SIZE : docSize;
     }
 
     protected abstract String getQueryBody(String queryText);

ml-algorithms/src/main/java/org/opensearch/ml/engine/tools/RAGTool.java

@@ -0,0 +1,309 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package org.opensearch.ml.engine.tools;
+
+import static org.apache.commons.text.StringEscapeUtils.escapeJson;
+import static org.opensearch.ml.common.utils.StringUtils.gson;
+
+import java.io.IOException;
+import java.security.AccessController;
+import java.security.PrivilegedActionException;
+import java.security.PrivilegedExceptionAction;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.commons.lang3.StringUtils;
+import org.opensearch.action.ActionRequest;
+import org.opensearch.action.search.SearchRequest;
+import org.opensearch.action.search.SearchResponse;
+import org.opensearch.client.Client;
+import org.opensearch.common.xcontent.LoggingDeprecationHandler;
+import org.opensearch.common.xcontent.XContentType;
+import org.opensearch.core.action.ActionListener;
+import org.opensearch.core.xcontent.NamedXContentRegistry;
+import org.opensearch.core.xcontent.XContentParser;
+import org.opensearch.ml.common.FunctionName;
+import org.opensearch.ml.common.dataset.remote.RemoteInferenceInputDataSet;
+import org.opensearch.ml.common.input.MLInput;
+import org.opensearch.ml.common.output.model.ModelTensor;
+import org.opensearch.ml.common.output.model.ModelTensorOutput;
+import org.opensearch.ml.common.output.model.ModelTensors;
+import org.opensearch.ml.common.spi.tools.Parser;
+import org.opensearch.ml.common.spi.tools.ToolAnnotation;
+import org.opensearch.ml.common.transport.MLTaskResponse;
+import org.opensearch.ml.common.transport.prediction.MLPredictionTaskAction;
+import org.opensearch.ml.common.transport.prediction.MLPredictionTaskRequest;
+import org.opensearch.search.SearchHit;
+import org.opensearch.search.builder.SearchSourceBuilder;
+
+import lombok.Builder;
+import lombok.Getter;
+import lombok.Setter;
+import lombok.extern.log4j.Log4j2;
+
+/**
+ * This tool supports running any ml-commons model.
+ */
+@Log4j2
+@Setter
+@Getter
+@ToolAnnotation(RAGTool.TYPE)
+public class RAGTool extends AbstractRetrieverTool {
+    public static final String TYPE = "RAGTool";
+    private static String DEFAULT_DESCRIPTION = "Use this tool to run any model.";
+    public static final String MODEL_ID_FIELD = "model_id";
+    public static final String EMBEDDING_MODEL_ID_FIELD = "embedding_model_id";
+    public static final String EMBEDDING_FIELD = "embedding_field";
+    public static final String OUTPUT_FIELD = "output_field";
+    private String name = TYPE;
+    private String description = DEFAULT_DESCRIPTION;
+    private Client client;
+    private String modelId;
+
+    private NamedXContentRegistry xContentRegistry;
+    private String index;
+    private String embeddingField;
+    private String[] sourceFields;
+    private String embeddingModelId;
+    private Integer docSize;
+    private Integer k;
+    @Setter
+    private Parser inputParser;
+    @Setter
+    private Parser outputParser;
+
+    @Builder
+    public RAGTool(
+        Client client,
+        NamedXContentRegistry xContentRegistry,
+        String index,
+        String embeddingField,
+        String[] sourceFields,
+        Integer k,
+        Integer docSize,
+        String embeddingModelId,
+        String modelId
+    ) {
+        super(client, xContentRegistry, index, sourceFields, docSize);
+        this.client = client;
+        this.xContentRegistry = xContentRegistry;
+        this.index = index;
+        this.embeddingField = embeddingField;
+        this.sourceFields = sourceFields;
+        this.embeddingModelId = embeddingModelId;
+        this.docSize = docSize == null ? DEFAULT_DOC_SIZE : docSize;
+        this.k = k == null ? DEFAULT_K : k;
+        this.modelId = modelId;
+
+        outputParser = new Parser() {
+            @Override
+            public Object parse(Object o) {
+                List<ModelTensors> mlModelOutputs = (List<ModelTensors>) o;
+                return mlModelOutputs.get(0).getMlModelTensors().get(0).getDataAsMap().get("response");
+            }
+        };
+    }
+
+    @Override
+    protected String getQueryBody(String queryText) {
+        if (StringUtils.isBlank(embeddingField) || StringUtils.isBlank(embeddingModelId)) {
+            throw new IllegalArgumentException(
+                "Parameter [" + EMBEDDING_FIELD + "] and [" + EMBEDDING_MODEL_ID_FIELD + "] can not be null or empty."
+            );
+        }
+        return "{\"query\":{\"neural\":{\""
+            + embeddingField
+            + "\":{\"query_text\":\""
+            + queryText
+            + "\",\"model_id\":\""
+            + embeddingModelId
+            + "\",\"k\":"
+            + k
+            + "}}}"
+            + " }";
+    }
+
+    @Override
+    public <T> void run(Map<String, String> parameters, ActionListener<T> listener) {
+        try {
+            String question = parameters.get(INPUT_FIELD);
+            try {
+                question = gson.fromJson(question, String.class);
+            } catch (Exception e) {
+                // throw new IllegalArgumentException("wrong input");
+            }
+            String query = getQueryBody(question);
+            if (StringUtils.isBlank(query)) {
+                throw new IllegalArgumentException("[" + INPUT_FIELD + "] is null or empty, can not process it.");
+            }
+            SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
+            XContentParser queryParser = XContentType.JSON
+                .xContent()
+                .createParser(xContentRegistry, LoggingDeprecationHandler.INSTANCE, query);
+            searchSourceBuilder.parseXContent(queryParser);
+            searchSourceBuilder.fetchSource(sourceFields, null);
+            searchSourceBuilder.size(docSize);
+            SearchRequest searchRequest = new SearchRequest().source(searchSourceBuilder).indices(index);
+            ActionListener actionListener = ActionListener.<SearchResponse>wrap(r -> {
+                SearchHit[] hits = r.getHits().getHits();
+                T vectorDBToolOutput;
+
+                if (hits != null && hits.length > 0) {
+                    StringBuilder contextBuilder = new StringBuilder();
+                    for (SearchHit hit : hits) {
+                        String doc = AccessController.doPrivileged((PrivilegedExceptionAction<String>) () -> {
+                            Map<String, Object> docContent = new HashMap<>();
+                            docContent.put("_id", hit.getId());
+                            docContent.put("_source", hit.getSourceAsMap());
+                            return gson.toJson(docContent);
+                        });
+                        contextBuilder.append(doc).append("\n");
+                    }
+                    vectorDBToolOutput = (T) gson.toJson(contextBuilder.toString());
+                } else {
+                    vectorDBToolOutput = (T) "";
+                }
+
+                Map<String, String> tmpParameters = new HashMap<>();
+                tmpParameters.putAll(parameters);
+
+                if (vectorDBToolOutput instanceof List
+                    && !((List) vectorDBToolOutput).isEmpty()
+                    && ((List) vectorDBToolOutput).get(0) instanceof ModelTensors) {
+                    ModelTensors tensors = (ModelTensors) ((List) vectorDBToolOutput).get(0);
+                    Object response = tensors.getMlModelTensors().get(0).getDataAsMap().get("response");
+                    tmpParameters.put(OUTPUT_FIELD, response + "");
+                } else if (vectorDBToolOutput instanceof ModelTensor) {
+                    tmpParameters.put(OUTPUT_FIELD, escapeJson(toJson(((ModelTensor) vectorDBToolOutput).getDataAsMap())));
+                } else {
+                    if (vectorDBToolOutput instanceof String) {
+                        tmpParameters.put(OUTPUT_FIELD, (String) vectorDBToolOutput);
+                    } else {
+                        tmpParameters.put(OUTPUT_FIELD, escapeJson(toJson(vectorDBToolOutput.toString())));
+                    }
+                }
+
+                RemoteInferenceInputDataSet inputDataSet = RemoteInferenceInputDataSet.builder().parameters(tmpParameters).build();
+                ActionRequest request = new MLPredictionTaskRequest(
+                    modelId,
+                    MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(inputDataSet).build()
+                );
+                client.execute(MLPredictionTaskAction.INSTANCE, request, ActionListener.<MLTaskResponse>wrap(resp -> {
+                    ModelTensorOutput modelTensorOutput = (ModelTensorOutput) resp.getOutput();
+                    modelTensorOutput.getMlModelOutputs();
+                    if (outputParser == null) {
+                        listener.onResponse((T) modelTensorOutput.getMlModelOutputs());
+                    } else {
+                        listener.onResponse((T) outputParser.parse(modelTensorOutput.getMlModelOutputs()));
+                    }
+                }, e -> {
+                    log.error("Failed to run model " + modelId, e);
+                    listener.onFailure(e);
+                }));
+            }, e -> {
+                log.error("Failed to search index", e);
+                listener.onFailure(e);
+            });
+            client.search(searchRequest, actionListener);
+        } catch (IOException e) {
+            throw new RuntimeException(e);
+        }
+    }
+
+    @Override
+    public String getType() {
+        return TYPE;
+    }
+
+    @Override
+    public String getName() {
+        return this.name;
+    }
+
+    @Override
+    public void setName(String s) {
+        this.name = s;
+    }
+
+    @Override
+    public boolean validate(Map<String, String> parameters) {
+        if (parameters == null || parameters.size() == 0) {
+            return false;
+        }
+        String question = parameters.get(INPUT_FIELD);
+        return question != null;
+    }
+
+    public static class Factory extends AbstractRetrieverTool.Factory<RAGTool> {
+        private Client client;
+        private NamedXContentRegistry xContentRegistry;
+
+        private static Factory INSTANCE;
+
+        public static Factory getInstance() {
+            if (INSTANCE != null) {
+                return INSTANCE;
+            }
+            synchronized (RAGTool.class) {
+                if (INSTANCE != null) {
+                    return INSTANCE;
+                }
+                INSTANCE = new Factory();
+                return INSTANCE;
+            }
+        }
+
+        public void init(Client client, NamedXContentRegistry xContentRegistry) {
+            this.client = client;
+            this.xContentRegistry = xContentRegistry;
+        }
+
+        @Override
+        public RAGTool create(Map<String, Object> params) {
+            String embeddingModelId = (String) params.get(EMBEDDING_MODEL_ID_FIELD);
+            String index = (String) params.get(INDEX_FIELD);
+            String embeddingField = (String) params.get(EMBEDDING_FIELD);
+            String[] sourceFields = gson.fromJson((String) params.get(SOURCE_FIELD), String[].class);
+            String modelId = (String) params.get(MODEL_ID_FIELD);
+            Integer docSize = params.containsKey(DOC_SIZE_FIELD) ? Integer.parseInt((String) params.get(DOC_SIZE_FIELD)) : 2;
+            return RAGTool
+                .builder()
+                .client(client)
+                .xContentRegistry(xContentRegistry)
+                .index(index)
+                .embeddingField(embeddingField)
+                .sourceFields(sourceFields)
+                .embeddingModelId(embeddingModelId)
+                .docSize(docSize)
+                .modelId(modelId)
+                .build();
+        }
+
+        @Override
+        public String getDefaultDescription() {
+            return DEFAULT_DESCRIPTION;
+        }
+    }
+
+    private String toJson(Object value) {
+        return getString(value);
+    }
+
+    public static String getString(Object value) {
+        try {
+            return AccessController.doPrivileged((PrivilegedExceptionAction<String>) () -> {
+                if (value instanceof String) {
+                    return (String) value;
+                } else {
+                    return gson.toJson(value);
+                }
+            });
+        } catch (PrivilegedActionException e) {
+            throw new RuntimeException(e);
+        }
+    }
+}

ml-algorithms/src/main/java/org/opensearch/ml/engine/tools/VectorDBTool.java

@@ -49,7 +49,7 @@ public VectorDBTool(
         super(client, xContentRegistry, index, sourceFields, docSize);
         this.modelId = modelId;
         this.embeddingField = embeddingField;
-        this.k = k == null ? 10 : k;
+        this.k = k == null ? DEFAULT_K : k;
     }
 
     @Override

plugin/src/main/java/org/opensearch/ml/plugin/MachineLearningPlugin.java

@@ -516,6 +516,7 @@ public Collection<Object> createComponents(
         CatIndexTool.Factory.getInstance().init(client, clusterService);
         PainlessScriptTool.Factory.getInstance().init(client, scriptService);
         VisualizationsTool.Factory.getInstance().init(client);
+        RAGTool.Factory.getInstance().init(client, xContentRegistry);
         SearchAlertsTool.Factory.getInstance().init(client);
         IndexMappingTool.Factory.getInstance().init(client);
 
@@ -529,6 +530,7 @@ public Collection<Object> createComponents(
         toolFactories.put(VisualizationsTool.TYPE, VisualizationsTool.Factory.getInstance());
         toolFactories.put(SearchAlertsTool.TYPE, SearchAlertsTool.Factory.getInstance());
         toolFactories.put(IndexMappingTool.NAME, IndexMappingTool.Factory.getInstance());
+        toolFactories.put(RAGTool.TYPE, RAGTool.Factory.getInstance());
 
         if (externalToolFactories != null) {
             toolFactories.putAll(externalToolFactories);