We utilize Retrieval Augmented Generation (RAG) technology to develop structured, precise, and current legal information datasets. This approach incorporates legal texts into a graph database and transforms them into vectors, allowing for accurate and relevant legal searches and contextual responses using large language models (LLM) such as ChatGPT.

The system addresses critical issues of data accuracy and relevance in legal contexts by automatically updating and structuring legal information from authoritative sources such as official database of European legislation (Euro Lex). This ensures that users receive reliable and timely legal advice, reducing the risk of hallucinations, errors and outdated information.

Contextual Integration with Internal Data:
LexRAG can provide context based on both public and internal information, making it highly adaptable for organizations. This capability allows businesses and legal professionals to integrate their internal documents into the system, enhancing the relevance and applicability of the contextual responses generated by LLMs.