Abhay-J-Kashyap/Agentic-RAG-System
An open-source, fully local Agentic RAG system for Indian Law. Features LangGraph multi-agent orchestration, sequential tool-calling, and a DeBERTa NLI guardrail to eliminate LLM hallucinations on consumer-grade GPUs.
SUMMARY AI summary by gpt-5-mini
Local, multi-agent Retrieval-Augmented Generation (RAG) pipeline for Indian statutes (IPC, CrPC, MVA, CPC, IDA). Built to run offline on consumer GPUs, it targets two core problems in legal AI: LLM hallucinations and VRAM limits. Intended for legal practitioners, researchers, and developers needing accurate, explainable statutory retrieval and reasoning. Key features: - LangGraph multi-agent orchestration for iterative research and answer aggregation - DeBERTa Cross-Encoder NLI guardrail that scores contradictions to flag hallucinations - Sequential tool calling to avoid Vulkan/GPU OOMs - Parent-child chunking to preserve provisos and exceptions in retrieval - Qdrant vector DB, all-mpnet-base-v2 embeddings, Qwen 2.5 (7B) LLM, and a Gradio "glass-box" UI streaming agent actions.
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Dates
| Created on GitHub | 2026-05-04 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-05-09 15:42 |
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