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adding multi-modal pre-processor for cohere #3219
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50 changes: 50 additions & 0 deletions
50
...ml/common/connector/functions/preprocess/CohereMultiModalEmbeddingPreProcessFunction.java
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,50 @@ | ||
| /* | ||
| * Copyright OpenSearch Contributors | ||
| * SPDX-License-Identifier: Apache-2.0 | ||
| */ | ||
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| package org.opensearch.ml.common.connector.functions.preprocess; | ||
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| import static org.opensearch.ml.common.utils.StringUtils.convertScriptStringToJsonString; | ||
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| import java.util.HashMap; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
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| import org.opensearch.ml.common.dataset.TextDocsInputDataSet; | ||
| import org.opensearch.ml.common.dataset.remote.RemoteInferenceInputDataSet; | ||
| import org.opensearch.ml.common.input.MLInput; | ||
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| public class CohereMultiModalEmbeddingPreProcessFunction extends ConnectorPreProcessFunction { | ||
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| public CohereMultiModalEmbeddingPreProcessFunction() { | ||
| this.returnDirectlyForRemoteInferenceInput = true; | ||
| } | ||
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| @Override | ||
| public void validate(MLInput mlInput) { | ||
| validateTextDocsInput(mlInput); | ||
| List<String> docs = ((TextDocsInputDataSet) mlInput.getInputDataset()).getDocs(); | ||
| if (docs.isEmpty() || (docs.size() == 1 && docs.getFirst() == null)) { | ||
| throw new IllegalArgumentException("No image provided"); | ||
| } | ||
| } | ||
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| @Override | ||
| public RemoteInferenceInputDataSet process(MLInput mlInput) { | ||
| TextDocsInputDataSet inputData = (TextDocsInputDataSet) mlInput.getInputDataset(); | ||
| Map<String, String> parametersMap = new HashMap<>(); | ||
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| /** | ||
| * Cohere multi-modal model expects either image or texts, not both. | ||
| * For image, customer can use this pre-process function. For texts, customer can use | ||
| * connector.pre_process.cohere.embedding | ||
| * Cohere expects An array of image data URIs for the model to embed. Maximum number of images per call is 1. | ||
| */ | ||
| parametersMap.put("images", inputData.getDocs().getFirst()); | ||
| return RemoteInferenceInputDataSet | ||
| .builder() | ||
| .parameters(convertScriptStringToJsonString(Map.of("parameters", parametersMap))) | ||
| .build(); | ||
| } | ||
| } | ||
89 changes: 89 additions & 0 deletions
89
...ommon/connector/functions/preprocess/CohereMultiModalEmbeddingPreProcessFunctionTest.java
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,89 @@ | ||
| /* | ||
| * Copyright OpenSearch Contributors | ||
| * SPDX-License-Identifier: Apache-2.0 | ||
| */ | ||
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| package org.opensearch.ml.common.connector.functions.preprocess; | ||
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| import static org.junit.Assert.assertEquals; | ||
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| import java.util.ArrayList; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
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| import org.junit.Before; | ||
| import org.junit.Rule; | ||
| import org.junit.Test; | ||
| import org.junit.rules.ExpectedException; | ||
| import org.opensearch.ml.common.FunctionName; | ||
| import org.opensearch.ml.common.dataset.TextDocsInputDataSet; | ||
| import org.opensearch.ml.common.dataset.TextSimilarityInputDataSet; | ||
| import org.opensearch.ml.common.dataset.remote.RemoteInferenceInputDataSet; | ||
| import org.opensearch.ml.common.input.MLInput; | ||
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| public class CohereMultiModalEmbeddingPreProcessFunctionTest { | ||
| @Rule | ||
| public ExpectedException exceptionRule = ExpectedException.none(); | ||
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| CohereMultiModalEmbeddingPreProcessFunction function; | ||
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| TextSimilarityInputDataSet textSimilarityInputDataSet; | ||
| TextDocsInputDataSet textDocsInputDataSet; | ||
| RemoteInferenceInputDataSet remoteInferenceInputDataSet; | ||
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| MLInput textEmbeddingInput; | ||
| MLInput textSimilarityInput; | ||
| MLInput remoteInferenceInput; | ||
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| @Before | ||
| public void setUp() { | ||
| function = new CohereMultiModalEmbeddingPreProcessFunction(); | ||
| textSimilarityInputDataSet = TextSimilarityInputDataSet.builder().queryText("test").textDocs(List.of("hello")).build(); | ||
| textDocsInputDataSet = TextDocsInputDataSet.builder().docs(List.of("imageString")).build(); | ||
| remoteInferenceInputDataSet = RemoteInferenceInputDataSet.builder().parameters(Map.of("images", "value2")).build(); | ||
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| textEmbeddingInput = MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(textDocsInputDataSet).build(); | ||
| textSimilarityInput = MLInput.builder().algorithm(FunctionName.TEXT_SIMILARITY).inputDataset(textSimilarityInputDataSet).build(); | ||
| remoteInferenceInput = MLInput.builder().algorithm(FunctionName.REMOTE).inputDataset(remoteInferenceInputDataSet).build(); | ||
| } | ||
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| @Test | ||
| public void testProcess_whenNullInput_expectIllegalArgumentException() { | ||
| exceptionRule.expect(IllegalArgumentException.class); | ||
| exceptionRule.expectMessage("Preprocess function input can't be null"); | ||
| function.apply(null); | ||
| } | ||
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| @Test | ||
| public void testProcess_whenWrongInput_expectIllegalArgumentException() { | ||
| exceptionRule.expect(IllegalArgumentException.class); | ||
| exceptionRule.expectMessage("This pre_process_function can only support TextDocsInputDataSet"); | ||
| function.apply(textSimilarityInput); | ||
| } | ||
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| @Test | ||
| public void testProcess_whenCorrectInput_expectCorrectOutput() { | ||
| MLInput mlInput = MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(textDocsInputDataSet).build(); | ||
| RemoteInferenceInputDataSet dataSet = function.apply(mlInput); | ||
| assertEquals(1, dataSet.getParameters().size()); | ||
| assertEquals("imageString", dataSet.getParameters().get("images")); | ||
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| } | ||
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| @Test | ||
| public void testProcess_whenInputTextIsnull_expectIllegalArgumentException() { | ||
| exceptionRule.expect(IllegalArgumentException.class); | ||
| exceptionRule.expectMessage("No image provided"); | ||
| List<String> docs = new ArrayList<>(); | ||
| docs.add(null); | ||
| TextDocsInputDataSet textDocsInputDataSet1 = TextDocsInputDataSet.builder().docs(docs).build(); | ||
| MLInput mlInput = MLInput.builder().algorithm(FunctionName.TEXT_EMBEDDING).inputDataset(textDocsInputDataSet1).build(); | ||
| RemoteInferenceInputDataSet dataSet = function.apply(mlInput); | ||
| } | ||
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| @Test | ||
| public void testProcess_whenRemoteInferenceInput_expectRemoteInferenceInputDataSet() { | ||
| RemoteInferenceInputDataSet dataSet = function.apply(remoteInferenceInput); | ||
| assertEquals(remoteInferenceInputDataSet, dataSet); | ||
| } | ||
| } |
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Only support image input? Should we also consider support text input ?
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Cohere multi-modal doesn't support text with image input. Either image input or text input. Example notebook
For text input, we could use our regular one:
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Got it. Add some java doc to explain this?
Does the model support multiple images or just one ?
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Just one per request.
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An array of image data URIs for the model to embed. Maximum number of images per call is 1.
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Got it, suggest add more java doc to explain these details