Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents
This is an incremental improvement for contract management professionals, offering enhanced information retrieval from mixed data sources.
The researchers tackled the problem of contract management by developing a Q&A system that combines information from PDF documents and databases using LLMs, RAG, text-to-SQL, and agents, which significantly improved answer relevance and accuracy without retraining the model.
We present a question-and-answer (Q\&A) application designed to support the contract management process by leveraging combined information from contract documents (PDFs) and data retrieved from contract management systems (database). This data is processed by a large language model (LLM) to provide precise and relevant answers. The accuracy of these responses is further enhanced through the use of Retrieval-Augmented Generation (RAG), text-to-SQL techniques, and agents that dynamically orchestrate the workflow. These techniques eliminate the need to retrain the language model. Additionally, we employed Prompt Engineering to fine-tune the focus of responses. Our findings demonstrate that this multi-agent orchestration and combination of techniques significantly improve the relevance and accuracy of the answers, offering a promising direction for future information systems.