Building A Legal RAG App in 36 Hours

Legal research, traditionally a complex and time-consuming process, can be significantly expedited using advanced retrieval-augmented generation (RAG) systems, as demonstrated by Weaviate's approach to creating a legal assistant in just 36 hours. This system leverages a Query Agent to process legal documents, using a reasoning layer that mimics human researchers to perform tasks such as schema inspection, structured query construction, and precision reranking, thus improving the accuracy and relevance of search results. The architecture involves embedding legal PDFs into Weaviate with a multivector model, dividing them into distinct collections for better search efficiency. The Query Agent operates in two modes: Search Mode, for retrieving and reranking relevant contract sections, and Ask Mode, for synthesizing answers from retrieved context. This method enhances transparency and reduces errors by constraining generated answers to the retrieved context, fostering efficiency and precision in legal research applications.

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