GH-1826 Fix EmbeddingModel's usage on Document#embedding

- Since the Document object's reference to the `embedding` is deprecated and will be removed, the VectorStore implementations require a way to store the embedding of the corresponding Document objects

     - One way to fix this is, to have the EmbeddingModel#embed to return the embeddings in the same order as that of the Documents passed to it.

       - Since both the Document and embedding collections use the List object, their iteration operation will make sure to keep them in line with the same order.

       - A fix is required to preserve the order when batching strategy is applied.
	  - Updated the Javadoc for BatchingStrategy
          - Fixed the Document List order in TokenCountBatchingStrategy

    - Refactored the vector store implementations to update this change

Resolves #GH-1826

vector-stores/spring-ai-pgvector-store/src/main/java/org/springframework/ai/vectorstore/PgVectorStore.java

@@ -196,10 +196,11 @@ public class PgVectorStore extends AbstractObservationVectorStore implements Ini
 
 	@Override
 	public void doAdd(List<Document> documents) {
-		this.embeddingModel.embed(documents, EmbeddingOptionsBuilder.builder().build(), this.batchingStrategy);
+		List<float[]> embeddings = this.embeddingModel.embed(documents, EmbeddingOptionsBuilder.builder().build(),
+				this.batchingStrategy);
 
 		List<List<Document>> batchedDocuments = batchDocuments(documents);
-		batchedDocuments.forEach(this::insertOrUpdateBatch);
+		batchedDocuments.forEach(batchDocument -> insertOrUpdateBatch(batchDocument, documents, embeddings));
 	}
 
 	private List<List<Document>> batchDocuments(List<Document> documents) {
@@ -210,7 +211,7 @@ public class PgVectorStore extends AbstractObservationVectorStore implements Ini
 		return batches;
 	}
 
-	private void insertOrUpdateBatch(List<Document> batch) {
+	private void insertOrUpdateBatch(List<Document> batch, List<Document> documents, List<float[]> embeddings) {
 		String sql = "INSERT INTO " + getFullyQualifiedTableName()
 				+ " (id, content, metadata, embedding) VALUES (?, ?, ?::jsonb, ?) " + "ON CONFLICT (id) DO "
 				+ "UPDATE SET content = ? , metadata = ?::jsonb , embedding = ? ";
@@ -223,7 +224,7 @@ public class PgVectorStore extends AbstractObservationVectorStore implements Ini
 				var document = batch.get(i);
 				var content = document.getContent();
 				var json = toJson(document.getMetadata());
-				var embedding = document.getEmbedding();
+				var embedding = embeddings.get(documents.indexOf(document));
 				var pGvector = new PGvector(embedding);
 
 				StatementCreatorUtils.setParameterValue(ps, 1, SqlTypeValue.TYPE_UNKNOWN,
@@ -499,23 +500,18 @@ public class PgVectorStore extends AbstractObservationVectorStore implements Ini
 			String id = rs.getString(COLUMN_ID);
 			String content = rs.getString(COLUMN_CONTENT);
 			PGobject pgMetadata = rs.getObject(COLUMN_METADATA, PGobject.class);
-			PGobject embedding = rs.getObject(COLUMN_EMBEDDING, PGobject.class);
 			Float distance = rs.getFloat(COLUMN_DISTANCE);
 
 			Map<String, Object> metadata = toMap(pgMetadata);
 			metadata.put(DocumentMetadata.DISTANCE.value(), distance);
 
+			// @formatter:off
 			return Document.builder()
 				.id(id)
 				.content(content)
 				.metadata(metadata)
 				.score(1.0 - distance)
-				.embedding(toFloatArray(embedding))
-				.build();
-		}
-
-		private float[] toFloatArray(PGobject embedding) throws SQLException {
-			return new PGvector(embedding.getValue()).toArray();
+				.build(); // @formatter:on
 		}
 
 		private Map<String, Object> toMap(PGobject pgObject) {

vector-stores/spring-ai-pgvector-store/src/test/java/org/springframework/ai/vectorstore/PgVectorStoreWithChatMemoryAdvisorIT.java

@@ -146,9 +146,6 @@ class PgVectorStoreWithChatMemoryAdvisorIT {
 		EmbeddingModel embeddingModel = mock(EmbeddingModel.class);
 
 		Mockito.doAnswer(invocationOnMock -> {
-			Object[] arguments = invocationOnMock.getArguments();
-			List<Document> documents = (List<Document>) arguments[0];
-			documents.forEach(d -> d.setEmbedding(this.embed));
 			return List.of(this.embed, this.embed);
 		}).when(embeddingModel).embed(ArgumentMatchers.any(), any(), any());
 		given(embeddingModel.embed(any(String.class))).willReturn(this.embed);