A Retrieval-Augmented Legal Intelligence System
This is a Retrieval-Augmented Generation (RAG) system designed to provide accurate and context-aware legal assistance for German law. The platform integrates statutory legislation from the German Bundestag and real-world court decisions from OpenLegalData through a hybrid retrieval architecture.
By combining multilingual embeddings, vector search, LLM-based query classification, and conversational memory, the system delivers grounded legal answers while significantly reducing hallucinations commonly observed in standalone language models.
Python LangChain LangGraph ChromaDB GPT-4o-mini Django Docker
- Hybrid retrieval across German statutory laws and legal cases
- LLM-based query classification with 90.6% precision
- Multilingual legal embeddings using multilingual-e5-large-instruct
- Conversation memory powered by LangGraph
- Retrieval-grounded generation to reduce hallucinations
- Django-based interactive legal assistant interface