Key improvements and explanations:

* **Clear Structure and Comments:**  The code is well-structured with comments explaining each step of the RAG process.  This makes it much easier to understand and maintain.
* **Configuration:** The `RAGQueryEngine` class is highly configurable through its constructor.  This allows you to easily adjust the behavior of the engine by changing the parameters.  This is crucial for production environments.
* **Error Handling:** Includes `try...except` blocks to catch potential exceptions during response generation and the overall query process.  This prevents the application from crashing and provides more informative error messages.  Logging is used to record errors for debugging.
* **Logging:**  Uses the `logging` module to provide informative messages about the RAG process.  This is essential for debugging and monitoring the engine in a production environment.  Logs include query expansion, retrieved indices, assembled context, source IDs, confidence score, and any errors encountered.
* **Query Expansion:** Implements query expansion using WordNet synonyms to broaden the search. This helps to retrieve more relevant documents.  The number of synonyms can be controlled.
* **Hybrid Retrieval:** Combines semantic search (using SentenceTransformers) and BM25 retrieval. This leverages the strengths of both methods to improve retrieval accuracy.  A simple rank averaging is used to combine the results.
* **Reranking:** Uses a cross-encoder to rerank the retrieved documents, improving the accuracy of the results.  Cross-encoders are generally more accurate than bi-encoders (like SentenceTransformers) but are also slower.
* **MMR (Maximum Marginal Relevance):**  Implements MMR to promote diversity in the retrieved documents. This helps to avoid redundancy and provide a more comprehensive context for the language model.  The `diversity` parameter controls the trade-off between relevance and diversity.
* **Context Assembly:** Implements smart context window packing to maximize the amount of relevant information that can be included in the context.  It also includes source deduplication to prevent redundant information from being added to the context.  A `max_context_length` parameter limits the size of the context.
* **Citation Tracking:** Keeps track of the document IDs that contribute to the context and includes them as citations in the generated response.
* **Context-Grounded Responses:**  Prompts the language model to answer the question based on the provided context.
* **Citation Injection:** Injects citations into the generated response.
* **Confidence Scoring:** Includes a placeholder for confidence scoring.  In a real-world scenario, you would replace this with a more sophisticated method to estimate the confidence of the response.
* **Device Management:**  The code automatically detects whether a GPU is available and uses it if possible.  This can significantly improve performance.  You can also explicitly specify the device to use.
* **Pre-computed Embeddings:** Embeddings for the documents are pre-computed during initialization. This speeds up the retrieval process.
* **Clear `aiva_query` Function:** The `aiva_query` function encapsulates the entire RAG process, making it easy to integrate with AIVA.
* **Type Hints:** Uses type hints to improve code readability and maintainability.
* **Example Usage:** Includes an example usage section to demonstrate how to use the `RAGQueryEngine` class.
* **Production Considerations:**  The code is designed with production considerations in mind, such as error handling, logging, and configuration.
* **NLTK Dependency:** Requires the `nltk` library for tokenization and synonym finding. Make sure to install it: `pip install nltk` and download the required data: