Agentic RAG Framework with Multi-Agent Specialization for Hallucination-Mitigated Legal Retrieval and IPC-to-BNS Cross-Mapping

An agentic RAG framework with multi-agent specialisation for hallucination-mitigated legal retrieval and IPC-to-BNS cross-mapping.

An agentic RAG framework with multi-agent specialisation for hallucination-mitigated legal retrieval and IPC-to-BNS cross-mapping. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are retrieval over an indexed document collection that grounds the language model's answers, cooperating agents with defined roles and tools, text or speech data preparation, model training, fine-tuning or prompting, and evaluation with task-appropriate metrics. The reference build uses LangChain, FAISS, LLM APIs, Python, and Hugging Face Transformers, with a clear project structure, setup instructions and sample data so that it can be run and extended.