Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents

TL;DR

Contrato360 2.0 introduces a question-answer system that integrates contract documents and database data using LLMs, RAG, and agents to improve accuracy without retraining models.

Contribution

The paper presents a novel multi-agent system that combines document and database information with LLMs, enhancing contract management Q accuracy through dynamic orchestration and prompt engineering.

Findings

Significant improvement in answer relevance and accuracy.

Effective use of RAG and text-to-SQL techniques.

Multi-agent orchestration enhances system performance.

Abstract

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.