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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.17942 |
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| _version_ | 1866929646134624256 |
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| author | Seabra, Antony Cavalcante, Claudio Nepomuceno, Joao Lago, Lucas Ruberg, Nicolaas Lifschitz, Sergio |
| author_facet | Seabra, Antony Cavalcante, Claudio Nepomuceno, Joao Lago, Lucas Ruberg, Nicolaas Lifschitz, Sergio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17942 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents Seabra, Antony Cavalcante, Claudio Nepomuceno, Joao Lago, Lucas Ruberg, Nicolaas Lifschitz, Sergio Artificial Intelligence 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. |
| title | Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.17942 |