A low-cost plan for building a legal RAG AI assistant
A legal AI assistant for Indian law is being designed to turn plain-language incident descriptions into legal guidance. If someone says that a person broke into their house and stole a phone, the system should identify likely offenses, connect them to relevant legal sections, explain why those sections fit, suggest a general course of action, and cite the exact provisions used.
The first would include the Indian Constitution, Bharatiya Nyaya Sanhita, Bharatiya Nagarik Suraksha Sanhita, and Bharatiya Sakshya Adhiniyam. The main safety goal is to reduce by grounding answers in cited legal text.
The planned build starts with a strong RAG pipeline, then adds a for fact extraction, retrieval, legal reasoning, and response validation. The tool choices are aimed at staying near free-tier costs during development, including LlamaIndex or LangGraph, Qdrant Cloud Free, , local BGE embeddings and reranking, FastAPI, and Groq-.
Key points
- The system targets Indian law and plain-language incident descriptions.
- It should map incidents to offenses, legal sections, reasoning, procedure, and citations.
- Exact legal citations are meant to reduce .
- The planned architecture starts with RAG and later adds a .
- The stack is chosen to keep development costs low through free tiers and local models.