Add Batching strategy for embedding documents

 - When embedding documents, allow batching the documents using some criteria.
 - `BatchingStrategy` interface with a `TokenCountBatchingStrategy` implementation that uses
   the openai max input token size of 8191 as the default.
 - Add a default method in EmbeddingModel to embed document using this new batching strategy.
 - Change `MilvusVectorStore` to make use of this new batching API.
 - Adding unit tests for `TokenCountBatchingStrategy`.
 - Adding openai integration test to call the embed API that uses batching.

Resolves spring-projects#1214

Other vector stores will be updated seperately

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/embedding/EmbeddingIT.java

@@ -1,5 +1,5 @@
 /*
- * Copyright 2023 - 2024 the original author or authors.
+ * Copyright 2023-2024 the original author or authors.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -18,23 +18,33 @@
 import org.junit.jupiter.api.Test;
 
 import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
+
+import org.springframework.ai.document.Document;
 import org.springframework.ai.embedding.EmbeddingRequest;
 import org.springframework.ai.embedding.EmbeddingResponse;
+import org.springframework.ai.embedding.TokenCountBatchingStrategy;
 import org.springframework.ai.openai.OpenAiEmbeddingModel;
 import org.springframework.ai.openai.OpenAiEmbeddingOptions;
 import org.springframework.ai.openai.OpenAiTestConfiguration;
+import org.springframework.ai.openai.api.OpenAiApi;
 import org.springframework.ai.openai.testutils.AbstractIT;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.boot.test.context.SpringBootTest;
+import org.springframework.core.io.DefaultResourceLoader;
+import org.springframework.core.io.Resource;
 
+import java.nio.charset.StandardCharsets;
 import java.util.List;
 
 import static org.assertj.core.api.Assertions.assertThat;
+import static org.assertj.core.api.Assertions.assertThatThrownBy;
 
 @SpringBootTest(classes = OpenAiTestConfiguration.class)
 @EnabledIfEnvironmentVariable(named = "OPENAI_API_KEY", matches = ".+")
 class EmbeddingIT extends AbstractIT {
 
+	private Resource resource = new DefaultResourceLoader().getResource("classpath:text_source.txt");
+
 	@Autowired
 	private OpenAiEmbeddingModel embeddingModel;
 
@@ -53,6 +63,28 @@ void defaultEmbedding() {
 		assertThat(embeddingModel.dimensions()).isEqualTo(1536);
 	}
 
+	@Test
+	void embeddingBatchDocuments() throws Exception {
+		assertThat(embeddingModel).isNotNull();
+		List<float[]> embedded = this.embeddingModel.embed(
+				List.of(new Document("Hello world"), new Document("Hello Spring"), new Document("Hello Spring AI!")),
+				OpenAiEmbeddingOptions.builder().withModel(OpenAiApi.DEFAULT_EMBEDDING_MODEL).build(),
+				new TokenCountBatchingStrategy());
+		assertThat(embedded.size()).isEqualTo(3);
+		embedded.forEach(embedding -> assertThat(embedding.length).isEqualTo(this.embeddingModel.dimensions()));
+	}
+
+	@Test
+	void embeddingBatchDocumentsThatExceedTheLimit() throws Exception {
+		assertThat(embeddingModel).isNotNull();
+		String contentAsString = resource.getContentAsString(StandardCharsets.UTF_8);
+		assertThatThrownBy(() -> {
+			embeddingModel.embed(List.of(new Document("Hello World"), new Document(contentAsString)),
+					OpenAiEmbeddingOptions.builder().withModel(OpenAiApi.DEFAULT_EMBEDDING_MODEL).build(),
+					new TokenCountBatchingStrategy());
+		}).isInstanceOf(IllegalArgumentException.class);
+	}
+
 	@Test
 	void embedding3Large() {
 

spring-ai-core/src/main/java/org/springframework/ai/embedding/BatchingStrategy.java

@@ -0,0 +1,39 @@
+/*
+ * Copyright 2024 the original author or authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.springframework.ai.embedding;
+
+import java.util.List;
+
+import org.springframework.ai.document.Document;
+
+/**
+ * Contract for batching {@link Document} objects so that the call to embed them could be
+ * optimized.
+ *
+ * @author Soby Chacko
+ * @since 1.0.0
+ */
+public interface BatchingStrategy {
+
+	/**
+	 * {@link EmbeddingModel} implementations can call this method to optimize embedding
+	 * tokens. The incoming collection of {@link Document}s are split into su-batches.
+	 * @param documents to batch
+	 * @return a list of sub-batches that contain {@link Document}s.
+	 */
+	List<List<Document>> batch(List<Document> documents);
+
+}

spring-ai-core/src/main/java/org/springframework/ai/embedding/EmbeddingModel.java

@@ -19,10 +19,18 @@
 import org.springframework.ai.model.Model;
 import org.springframework.util.Assert;
 
+import java.util.ArrayList;
 import java.util.List;
 
 /**
  * EmbeddingModel is a generic interface for embedding models.
+ *
+ * @author Mark Pollack
+ * @author Christian Tzolov
+ * @author Josh Long
+ * @author Soby Chacko
+ * @since 1.0.0
+ *
  */
 public interface EmbeddingModel extends Model<EmbeddingRequest, EmbeddingResponse> {
 
@@ -61,6 +69,35 @@ default List<float[]> embed(List<String> texts) {
 			.toList();
 	}
 
+	/**
+	 * Embeds a batch of {@link Document}s into vectors based on a
+	 * {@link BatchingStrategy}.
+	 * @param documents list of {@link Document}s.
+	 * @param options {@link EmbeddingOptions}.
+	 * @param batchingStrategy {@link BatchingStrategy}.
+	 * @return a list of float[] that represents the vectors for the incoming
+	 * {@link Document}s.
+	 */
+	default List<float[]> embed(List<Document> documents, EmbeddingOptions options, BatchingStrategy batchingStrategy) {
+		Assert.notNull(documents, "Documents must not be null");
+		List<float[]> embeddings = new ArrayList<>();
+
+		List<List<Document>> batch = batchingStrategy.batch(documents);
+
+		for (List<Document> subBatch : batch) {
+			List<String> texts = subBatch.stream().map(Document::getContent).toList();
+			EmbeddingRequest request = new EmbeddingRequest(texts, options);
+			EmbeddingResponse response = this.call(request);
+			for (int i = 0; i < subBatch.size(); i++) {
+				Document document = subBatch.get(i);
+				float[] output = response.getResults().get(i).getOutput();
+				embeddings.add(output);
+				document.setEmbedding(output);
+			}
+		}
+		return embeddings;
+	}
+
 	/**
 	 * Embeds a batch of texts into vectors and returns the {@link EmbeddingResponse}.
 	 * @param texts list of texts to embed.

spring-ai-core/src/main/java/org/springframework/ai/embedding/TokenCountBatchingStrategy.java

@@ -0,0 +1,105 @@
+/*
+ * Copyright 2024 the original author or authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.springframework.ai.embedding;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import org.springframework.ai.document.ContentFormatter;
+import org.springframework.ai.document.Document;
+import org.springframework.ai.document.MetadataMode;
+import org.springframework.ai.tokenizer.JTokkitTokenCountEstimator;
+import org.springframework.ai.tokenizer.TokenCountEstimator;
+
+import com.knuddels.jtokkit.api.EncodingType;
+
+/**
+ * Token count based strategy implementation for {@link BatchingStrategy}. Using openai
+ * max input token as the default:
+ * https://platform.openai.com/docs/guides/embeddings/embedding-models.
+ *
+ * @author Soby Chacko
+ * @since 1.0.0
+ */
+public class TokenCountBatchingStrategy implements BatchingStrategy {
+
+	/**
+	 * Using openai upper limit of input token count as the default.
+	 */
+	private static final int MAX_INPUT_TOKEN_COUNT = 8191;
+
+	private final TokenCountEstimator tokenCountEstimator;
+
+	private final int maxInputTokenCount;
+
+	private final ContentFormatter contentFormater;
+
+	private final MetadataMode metadataMode;
+
+	public TokenCountBatchingStrategy() {
+		this(EncodingType.CL100K_BASE, MAX_INPUT_TOKEN_COUNT);
+	}
+
+	/**
+	 * @param encodingType {@link EncodingType}
+	 * @param maxInputTokenCount upper limit for input tokens
+	 */
+	public TokenCountBatchingStrategy(EncodingType encodingType, int maxInputTokenCount) {
+		this(encodingType, maxInputTokenCount, Document.DEFAULT_CONTENT_FORMATTER, MetadataMode.NONE);
+	}
+
+	/**
+	 * @param encodingType {@link EncodingType}
+	 * @param maxInputTokenCount upper limit for input tokens
+	 * @param contentFormatter {@link ContentFormatter}
+	 * @param metadataMode {@link MetadataMode}
+	 */
+	public TokenCountBatchingStrategy(EncodingType encodingType, int maxInputTokenCount,
+			ContentFormatter contentFormatter, MetadataMode metadataMode) {
+		this.tokenCountEstimator = new JTokkitTokenCountEstimator(encodingType);
+		this.maxInputTokenCount = (int) Math.round(maxInputTokenCount - (maxInputTokenCount * .1));
+		this.contentFormater = contentFormatter;
+		this.metadataMode = metadataMode;
+	}
+
+	@Override
+	public List<List<Document>> batch(List<Document> documents) {
+		List<List<Document>> batches = new ArrayList<>();
+		int currentSize = 0;
+		List<Document> currentBatch = new ArrayList<>();
+
+		for (Document document : documents) {
+			int tokenCount = this.tokenCountEstimator
+				.estimate(document.getFormattedContent(this.contentFormater, this.metadataMode));
+			if (tokenCount > this.maxInputTokenCount) {
+				throw new IllegalArgumentException(
+						"Tokens in a single document exceeds the maximum number of allowed input tokens");
+			}
+			if (currentSize + tokenCount > maxInputTokenCount) {
+				batches.add(currentBatch);
+				currentBatch.clear();
+				currentSize = 0;
+			}
+			currentBatch.add(document);
+			currentSize += tokenCount;
+		}
+		if (!currentBatch.isEmpty()) {
+			batches.add(currentBatch);
+		}
+		return batches;
+	}
+
+}

spring-ai-core/src/main/java/org/springframework/ai/tokenizer/JTokkitTokenCountEstimator.java

@@ -21,23 +21,27 @@
 import com.knuddels.jtokkit.api.EncodingType;
 
 import org.springframework.ai.model.Media;
-import org.springframework.ai.model.Content;
 import org.springframework.ai.model.MediaContent;
 import org.springframework.util.CollectionUtils;
 
 /**
+ * Estimates the number of tokens in a given text or message using the JTokkit encoding
+ * library.
+ *
  * @author Christian Tzolov
+ * @author Soby Chacko
+ * @since 1.0.0
  */
 public class JTokkitTokenCountEstimator implements TokenCountEstimator {
 
 	private final Encoding estimator;
 
 	public JTokkitTokenCountEstimator() {
-		this.estimator = Encodings.newLazyEncodingRegistry().getEncoding(EncodingType.CL100K_BASE);
+		this(EncodingType.CL100K_BASE);
 	}
 
-	public JTokkitTokenCountEstimator(Encoding tokenEncoding) {
-		this.estimator = tokenEncoding;
+	public JTokkitTokenCountEstimator(EncodingType tokenEncodingType) {
+		this.estimator = Encodings.newLazyEncodingRegistry().getEncoding(tokenEncodingType);
 	}
 
 	@Override

spring-ai-core/src/test/java/org/springframework/ai/embedding/TokenCountBatchingStrategyTests.java

@@ -0,0 +1,57 @@
+/*
+ * Copyright 2024-2024 the original author or authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.springframework.ai.embedding;
+
+import static org.assertj.core.api.Assertions.assertThat;
+import static org.assertj.core.api.Assertions.assertThatThrownBy;
+
+import java.io.IOException;
+import java.nio.charset.StandardCharsets;
+import java.util.List;
+
+import org.junit.jupiter.api.Test;
+
+import org.springframework.ai.document.Document;
+import org.springframework.core.io.DefaultResourceLoader;
+import org.springframework.core.io.Resource;
+
+/**
+ * Basic unit test for {@link TokenCountBatchingStrategy}.
+ *
+ * @author Soby Chacko
+ */
+public class TokenCountBatchingStrategyTests {
+
+	@Test
+	void batchEmbeddingHappyPath() {
+		TokenCountBatchingStrategy tokenCountBatchingStrategy = new TokenCountBatchingStrategy();
+		List<List<Document>> batch = tokenCountBatchingStrategy.batch(
+				List.of(new Document("Hello world"), new Document("Hello Spring"), new Document("Hello Spring AI!")));
+		assertThat(batch.size()).isEqualTo(1);
+		assertThat(batch.get(0).size()).isEqualTo(3);
+	}
+
+	@Test
+	void batchEmbeddingWithLargeDocumentExceedsMaxTokenSize() throws IOException {
+		Resource resource = new DefaultResourceLoader().getResource("classpath:text_source.txt");
+		String contentAsString = resource.getContentAsString(StandardCharsets.UTF_8);
+		TokenCountBatchingStrategy tokenCountBatchingStrategy = new TokenCountBatchingStrategy();
+		assertThatThrownBy(() -> {
+			tokenCountBatchingStrategy.batch(List.of(new Document(contentAsString)));
+		}).isInstanceOf(IllegalArgumentException.class);
+	}
+
+}