style: standardize code formatting and indentation across multiple files

Author: Aias00

src/main/java/com/langmem/mem4j/embeddings/DashScopeEmbeddingService.java

@@ -126,7 +126,7 @@ public Double[][] embed(String[] texts) {
 			JsonNode responseJson = objectMapper.readTree(response.getBody());
 			JsonNode embeddings = responseJson.path("output").path("embeddings");
 
-            Double[][] result = new Double[embeddings.size()][];
+			Double[][] result = new Double[embeddings.size()][];
 			for (int i = 0; i < embeddings.size(); i++) {
 				JsonNode embeddingArray = embeddings.get(i).path("embedding");
 				result[i] = new Double[embeddingArray.size()];

src/main/java/com/langmem/mem4j/embeddings/EmbeddingService.java

@@ -23,14 +23,14 @@
 public interface EmbeddingService {
 
 	/**
-     * Generate embedding for a text
-     */
-    Double[] embed(String text);
+	 * Generate embedding for a text
+	 */
+	Double[] embed(String text);
 
 	/**
-     * Generate embeddings for multiple texts
-     */
-    Double[][] embed(String[] texts);
+	 * Generate embeddings for multiple texts
+	 */
+	Double[][] embed(String[] texts);
 
 	/**
 	 * Get the dimension of embeddings

src/main/java/com/langmem/mem4j/embeddings/OpenAIEmbeddingService.java

@@ -62,8 +62,8 @@ public Double[] embed(String text) {
 			EmbeddingResult result = openAiService.createEmbeddings(request);
 
 			if (!result.getData().isEmpty()) {
-                List<Double> embedding = result.getData().get(0).getEmbedding();
-                return embedding.toArray(new Double[0]);
+				List<Double> embedding = result.getData().get(0).getEmbedding();
+				return embedding.toArray(new Double[0]);
 			}
 
 			throw new RuntimeException("No embedding generated");

src/main/java/com/langmem/mem4j/memory/Memory.java

@@ -78,7 +78,7 @@ public void add(List<Message> messages, String userId, Map<String, Object> metad
 
 			// Generate embeddings and store
 			for (MemoryItem item : memoryItems) {
-                Double[] embedding = embeddingService.embed(item.getContent());
+				Double[] embedding = embeddingService.embed(item.getContent());
 				item.setEmbedding(embedding);
 				vectorStoreService.add(item);
 			}
@@ -107,7 +107,7 @@ public List<MemoryItem> search(String query, String userId, Map<String, Object>
 
 		try {
 			// Generate embedding for query
-            Double[] queryEmbedding = embeddingService.embed(query);
+			Double[] queryEmbedding = embeddingService.embed(query);
 
 			// Build search filters
 			Map<String, Object> searchFilters = buildSearchFilters(userId, filters);
@@ -155,7 +155,7 @@ public void update(String memoryId, Map<String, Object> data) {
 				if (data.containsKey("content")) {
 					item.setContent((String) data.get("content"));
 					// Re-embed if content changed
-                    Double[] embedding = embeddingService.embed(item.getContent());
+					Double[] embedding = embeddingService.embed(item.getContent());
 					item.setEmbedding(embedding);
 				}
 				if (data.containsKey("metadata")) {

src/main/java/com/langmem/mem4j/vectorstores/InMemoryVectorStoreService.java

@@ -58,7 +58,8 @@ public void add(MemoryItem item) {
 	}
 
 	@Override
-	public List<MemoryItem> search(Double[] queryEmbedding, Map<String, Object> filters, Integer limit, Double threshold) {
+	public List<MemoryItem> search(Double[] queryEmbedding, Map<String, Object> filters, Integer limit,
+			Double threshold) {
 
 		try {
 			return memoryStore.values().stream().filter(item -> matchesFilters(item, filters)).map(item -> {
@@ -73,7 +74,7 @@ public List<MemoryItem> search(Double[] queryEmbedding, Map<String, Object> filt
 				result.setUpdatedAt(item.getUpdatedAt());
 
 				// Calculate similarity score
-                Double[] itemEmbedding = embeddings.get(item.getId());
+				Double[] itemEmbedding = embeddings.get(item.getId());
 				if (itemEmbedding != null) {
 					double similarity = cosineSimilarity(queryEmbedding, itemEmbedding);
 					result.setScore(similarity);

src/main/java/com/langmem/mem4j/vectorstores/QdrantVectorStoreService.java

@@ -110,15 +110,16 @@ public void add(MemoryItem item) {
 	}
 
 	@Override
-	public List<MemoryItem> search(Double[] queryEmbedding, Map<String, Object> filters, Integer limit, Double threshold) {
+	public List<MemoryItem> search(Double[] queryEmbedding, Map<String, Object> filters, Integer limit,
+			Double threshold) {
 		try {
 			// Simple similarity search using in-memory storage
 			List<MemoryItem> results = memoryStore.values()
 				.stream()
 				.filter(item -> matchesFilters(item, filters))
 				.map(item -> {
 					// Calculate simple cosine similarity
-                    Double similarity = calculateCosineSimilarity(queryEmbedding, item.getEmbedding());
+					Double similarity = calculateCosineSimilarity(queryEmbedding, item.getEmbedding());
 					// Store similarity for sorting
 					return new AbstractMap.SimpleEntry<>(similarity, item);
 				})
@@ -298,9 +299,9 @@ private Double calculateCosineSimilarity(Double[] vec1, Double[] vec2) {
 			return 0.0;
 		}
 
-        double dotProduct = 0.0;
-        double norm1 = 0.0;
-        double norm2 = 0.0;
+		double dotProduct = 0.0;
+		double norm1 = 0.0;
+		double norm2 = 0.0;
 
 		for (int i = 0; i < vec1.length; i++) {
 			dotProduct += vec1[i] * vec2[i];