JurisLibreIA Asistente Juridico Inteligente: Facilitando el acceso a la justicia (Presentación)
| dc.contributor.advisor | Gonzalez Torres, Daniel Leonardo | |
| dc.contributor.advisor | Santa Quintero, Ricardo Andres | |
| dc.contributor.author | Gonzalez Torres, Daniel Leonardo | |
| dc.contributor.author | Santa Quintero, Ricardo Andres | |
| dc.coverage.spatial | Bogotá | spa |
| dc.creator.email | daniell-gonzalezt@unilibre.edu.co | spa |
| dc.creator.email | ricardoa.santaq@unilibre.edu.co | spa |
| dc.date.accessioned | 2025-05-26T12:46:44Z | |
| dc.date.available | 2025-05-26T12:46:44Z | |
| dc.date.created | 2025-04-08 | |
| dc.description.abstract | La presentación se centro en Jurislibre IA, una plataforma de asesoría legal basada en IA que integra dos enfoques clave RAG (Retrieval-Augmented Generation) y fine-tuning de modelos de lenguaje junto a bases de datos vectoriales y de grafos. Se detalló en profundidad el funcionamiento de cada tecnología, los beneficios de combinar recuperación de información con generación, y por qué las estructuras de datos avanzadas resultaron cruciales. También se expusieron las pruebas iniciales en servidores locales, así como la transición hacia entornos en línea usando GPT Playground y librerías LLM de Python, con el objetivo de reforzar la seguridad y la confiabilidad para el usuario final. | spa |
| dc.description.abstractenglish | The presentation focused on Jurislibre AI, an AI-based legal advisory platform that integrates two key approaches: Retrieval-Augmented Generation (RAG) and fine-tuning of language models with vector and graph databases. The presentation detailed in depth how each technology works, the benefits of combining information retrieval with generation, and why advanced data structures were crucial. Initial testing on local servers was also discussed, as well as the transition to online environments using GPT Playground and Python LLM libraries, with the goal of strengthening security and reliability for the end user. | spa |
| dc.description.sponsorship | Universidad Libre -- Ingenieria -- Ingenieria de sistemas | spa |
| dc.format | spa | |
| dc.identifier.uri | https://hdl.handle.net/10901/31186 | |
| dc.relation.references | NVIDIA. (n.d.). What Is Retrieval-Augmented Generation (RAG)? NVIDIA. Retrieved May 13, 2025, from https://www.nvidia.com/en-us/glossary/retrieval-augmented-generation/ | spa |
| dc.relation.references | Singh, S., Khanuja, M., & Zhang, Y. (2024, August 5). Build an end-to-end RAG solution using Amazon Bedrock Knowledge Bases and AWS CloudFormation. AWS Machine Learning Blog. Retrieved May 13, 2025, from https://aws.amazon.com/blogs/machine-learning/build-an-end-to-end-rag-solution-using-knowledge-bases-for-amazon-bedrock-and-aws-cloudformation/ | spa |
| dc.relation.references | DataCamp. (n.d.). Fine-Tuning LLMs: A Guide With Examples. Retrieved May 13, 2025, from https://www.datacamp.com/tutorial/fine-tuning-large-language-models | spa |
| dc.relation.references | Martínez, J. (2022, October ? ). Supervised fine-tuning: Customizing LLMs. Medium. Retrieved May 13, 2025, from https://medium.com/mantisnlp/supervised-fine-tuning-customizing-llms-a2c1edbf22c3 | spa |
| dc.relation.references | Pinecone. (n.d.). Pinecone Database: Overview. Retrieved May 13, 2025, from https://docs.pinecone.io/guides/get-started/overview | spa |
| dc.relation.references | Elastic. (n.d.). Semantic Search. Elastic.co. Retrieved May 13, 2025, from https://www.elastic.co/guide/en/elasticsearch/reference/current/semantic-search.html | spa |
| dc.relation.references | Amazon Web Services. (n.d.). Amazon Neptune – Managed Graph Database. Retrieved May 13, 2025, from https://aws.amazon.com/neptune/ | spa |
| dc.relation.references | Neo4j. (n.d.). Graph Database Use Cases & Solutions. Retrieved May 13, 2025, from https://neo4j.com/use-cases/ | spa |
| dc.relation.references | OpenAI. (n.d.). OpenAI platform docs. Retrieved May 13, 2025, from https://platform.openai.com/docs | spa |
| dc.relation.references | LangChain. (n.d.). Introduction | LangChain. Retrieved May 13, 2025, from https://python.langchain.com/docs/introduction/ | spa |
| dc.relation.references | Cuantificación vectorial. (n.d.). Retrieved from https://academia-lab.com/enciclopedia/cuantificacion-vectorial/ | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.subject | Retrieval-Augmented Generation (RAG) | spa |
| dc.subject | Fine-tuning | spa |
| dc.subject | Bases de datos vectoriales | spa |
| dc.subject | Bases de datos de grafos | spa |
| dc.subject | GPT Playground | spa |
| dc.subject | Langchain | spa |
| dc.subject | Metodos de cuantizacion | spa |
| dc.subject | Embebbings | spa |
| dc.subject | Indexación semántica | spa |
| dc.subject | Inteligencia Artificial aplicada al derecho | spa |
| dc.subject.subjectenglish | Retrieval-Augmented Generation (RAG) | spa |
| dc.subject.subjectenglish | Fine-tuning | spa |
| dc.subject.subjectenglish | Vector database | spa |
| dc.subject.subjectenglish | Graph database | spa |
| dc.subject.subjectenglish | GPT Playground | spa |
| dc.subject.subjectenglish | Langchain | spa |
| dc.subject.subjectenglish | Quantization Methods | spa |
| dc.subject.subjectenglish | Embebbings | spa |
| dc.subject.subjectenglish | Semantic indexing | spa |
| dc.subject.subjectenglish | Intelligent Legal Services | spa |
| dc.title | JurisLibreIA Asistente Juridico Inteligente: Facilitando el acceso a la justicia (Presentación) | spa |
| dc.title.alternative | Presentation JurisLibreAI Smart Legal Assistant: Facilitating access to justice | spa |
| dc.type.driver | info:eu-repo/semantics/bachelorThesis | spa |
| dc.type.local | Tesis de Pregrado | spa |
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