Multi-Head RAG (MRAG) is a novel approach to enhance Retrieval Augmented Generation (RAG) for handling multi-aspect queries. Traditional RAG methods use embeddings from the last decoder layer, but MRAG leverages the activations from the multi-head attention layer, allowing the model to capture different aspects of data items and queries. This approach improves retrieval accuracy for complex, multi-aspect queries by using embeddings that represent various facets of the data. MRAG can be seamlessly integrated with existing RAG frameworks and benchmarking tools like RAGAS, and it has been shown to improve retrieval performance by up to 20% compared to standard RAG baselines. MRAG addresses the challenge of retrieving multiple documents with substantially different contents by using the multi-head attention layer's activations as embeddings. This method allows the model to capture different aspects of the data, leading to more accurate retrieval. The paper presents an evaluation methodology, synthetic datasets, and real-world use cases to demonstrate MRAG's effectiveness. The results show that MRAG outperforms standard RAG in retrieval accuracy, particularly for multi-aspect queries. The MRAG pipeline consists of two main parts: data preparation and query execution. During data preparation, multi-aspect embeddings are generated using a selected decoder-based embedding model. These embeddings are stored in a data store, with each multi-aspect embedding consisting of multiple single-aspect embeddings. During query execution, the input query is converted into a multi-aspect embedding, and the nearest multi-aspect embeddings are retrieved from the data store using a special retrieval strategy. The retrieved data is then assessed using novel metrics to evaluate how well it meets the multi-aspect requirements. The paper also discusses the integration of MRAG with different data stores and nearest neighbor search approaches. It presents a detailed analysis of the results, showing that MRAG outperforms standard RAG in retrieval accuracy across various aspects and embedding models. Additionally, the paper evaluates MRAG's performance in real-world use cases, such as the synthesis of legal documents and the analysis of causes of chemical plant accidents, demonstrating its effectiveness in practical scenarios. MRAG's approach is simple and versatile, allowing it to be seamlessly integrated into any modern RAG pipeline or data analytics framework. The results indicate that MRAG can significantly improve the relevance of retrieved documents, making it a valuable advancement in the field of LLMs and RAG systems. By addressing the challenges of multi-aspectuality in queries, MRAG paves the way for more reliable and accurate LLM applications across diverse industries.Multi-Head RAG (MRAG) is a novel approach to enhance Retrieval Augmented Generation (RAG) for handling multi-aspect queries. Traditional RAG methods use embeddings from the last decoder layer, but MRAG leverages the activations from the multi-head attention layer, allowing the model to capture different aspects of data items and queries. This approach improves retrieval accuracy for complex, multi-aspect queries by using embeddings that represent various facets of the data. MRAG can be seamlessly integrated with existing RAG frameworks and benchmarking tools like RAGAS, and it has been shown to improve retrieval performance by up to 20% compared to standard RAG baselines. MRAG addresses the challenge of retrieving multiple documents with substantially different contents by using the multi-head attention layer's activations as embeddings. This method allows the model to capture different aspects of the data, leading to more accurate retrieval. The paper presents an evaluation methodology, synthetic datasets, and real-world use cases to demonstrate MRAG's effectiveness. The results show that MRAG outperforms standard RAG in retrieval accuracy, particularly for multi-aspect queries. The MRAG pipeline consists of two main parts: data preparation and query execution. During data preparation, multi-aspect embeddings are generated using a selected decoder-based embedding model. These embeddings are stored in a data store, with each multi-aspect embedding consisting of multiple single-aspect embeddings. During query execution, the input query is converted into a multi-aspect embedding, and the nearest multi-aspect embeddings are retrieved from the data store using a special retrieval strategy. The retrieved data is then assessed using novel metrics to evaluate how well it meets the multi-aspect requirements. The paper also discusses the integration of MRAG with different data stores and nearest neighbor search approaches. It presents a detailed analysis of the results, showing that MRAG outperforms standard RAG in retrieval accuracy across various aspects and embedding models. Additionally, the paper evaluates MRAG's performance in real-world use cases, such as the synthesis of legal documents and the analysis of causes of chemical plant accidents, demonstrating its effectiveness in practical scenarios. MRAG's approach is simple and versatile, allowing it to be seamlessly integrated into any modern RAG pipeline or data analytics framework. The results indicate that MRAG can significantly improve the relevance of retrieved documents, making it a valuable advancement in the field of LLMs and RAG systems. By addressing the challenges of multi-aspectuality in queries, MRAG paves the way for more reliable and accurate LLM applications across diverse industries.