Imagine searching for information and instantly finding exactly what you need, even in a vast ocean of data. That's the promise of enhanced document retrieval, and researchers are making exciting strides using a clever technique called "topic embeddings." Traditional search methods often struggle when multiple related topics are mixed within a document collection. Think about searching a legal database—you might be looking for information on property law, but the results are cluttered with irrelevant cases about contract disputes. Topic embeddings offer a smarter solution by adding an extra layer of context to the search process. The research introduces two innovative approaches. The first method enhances document embeddings by weaving in relevant topic information, providing a more nuanced representation of each document. The second approach employs a two-stage retrieval system, first identifying the relevant topic and then pinpointing the specific document within that topic. To test these methods, researchers built a dataset from Azerbaijani law, dividing it into chunks and using OpenAI's embedding model. The results? A significant boost in accurately separating different legal topics, making searches far more precise. The average method, which combines document and topic embeddings, emerged as the winner. While the initial findings are promising, some challenges remain. A key hurdle is the need for high-quality evaluation datasets with natural queries, as synthetically generated queries aren’t quite up to par. Despite these challenges, the future of document retrieval looks bright. Topic embeddings offer a powerful tool to unlock the true potential of search, paving the way for more accurate, efficient, and insightful information access.