How can the Dubo-SQL methods be further improved to handle very large corporate databases with thousands of tables and columns?
To enhance the Dubo-SQL methods for handling very large corporate databases, several improvements can be considered:
Increased Context Window: Given the limitation of the context window in current models, utilizing models with larger context windows, such as gpt-3.5-turbo-0125, could allow for the inclusion of more extensive table schemas and sample data from larger databases.
Selective Table Schema Inclusion: Implementing a mechanism to select relevant tables from a vast database could help manage the token limits. This could involve a pre-processing step to identify the most pertinent tables based on the user query.
Efficient Data Representation: Developing more efficient ways to represent data to LLMs, such as using structured formats that are easily interpretable by the models, can help handle the complexity of large databases without overwhelming the context window.
Hybrid Approaches: Combining the strengths of fine-tuning and retrieval-augmented generation could provide a balanced solution. Fine-tuning on specific aspects of the task while leveraging retrieval-augmented generation for broader context understanding could improve performance on large databases.
Optimized Prompting Strategies: Refining the prompting strategies to provide the necessary context efficiently while maximizing the utilization of the available context window can also contribute to handling larger databases effectively.