Scaling the uRAG framework to accommodate a larger number of RAG models and tasks can pose several challenges and limitations. To address these issues, the following strategies can be implemented:
Resource Management: Efficiently manage computational resources to handle the increased workload of training and optimizing the search engine for a larger number of RAG models. Utilize distributed computing and parallel processing to scale the framework effectively.
Model Complexity: Simplify the architecture and design of the unified search engine to handle a larger number of RAG models and tasks. Consider optimizing the model for speed and efficiency without compromising performance.
Data Management: Develop robust data pipelines and data management strategies to handle the influx of data from multiple RAG models and tasks. Implement data preprocessing techniques to streamline the data ingestion process.
Evaluation and Monitoring: Establish comprehensive evaluation metrics and monitoring systems to track the performance of the unified search engine across multiple RAG models and tasks. Continuously assess the model's performance and make necessary adjustments to improve scalability.