NR10-RAG-Assistant: A Localized RAG Technical Assistant for Regulatory Documents

NR10-RAG-Assistant is a localized Retrieval-Augmented Generation (RAG) technical assistant designed specifically for Brazil's NR10 electrical safety regulatory documents. It employs hybrid search, embedding vectors, re-ranking, and local LLM inference technologies, balancing professional domain adaptation and data privacy protection, and demonstrates the application value of RAG technology in highly specialized regulatory fields.

Overview of the NR10 Standard

NR10 is a technical regulation on electrical installation safety formulated by Brazil's Ministry of Labor, covering electrical installation safety, work permit systems, personal protective equipment, emergency procedures, and training requirements.

Technical Challenges of Regulatory Documents

Such documents pose special challenges to RAG systems: dense professional terminology, complex structured content (clause hierarchy), numerous cross-references, difficult version management, and the need to support multiple languages (e.g., Portuguese).

Hybrid Search Architecture

Combines dense retrieval (fine-tuned embedding model + vector database) and sparse retrieval (BM25 + keyword matching), fuses results via RRF (Reciprocal Rank Fusion) and dynamically adjusts weights.

Document Processing Pipeline

Includes PDF parsing (extracting text/structure/metadata), structure-aware semantic chunking (maintaining clause integrity), and vectorization processing (multilingual model + batch processing).

Re-ranking System

Uses cross-encoders for fine-grained matching of Top-K candidates, optimizes relevance by combining term weighting and structural features.

Local LLM Inference

Uses open-source models (Llama/Mistral) with quantization optimization, deployed locally via llama.cpp/Ollama, combined with prompt engineering (context injection + citation requirements) and safety compliance measures (content filtering + audit logs).

1. Domain-Adapted Embedding Model: Fine-tuned models enhance professional terminology understanding, semantic alignment, and multilingual support capabilities.
2. Structured RAG: Supports hierarchical retrieval, reference parsing, and regulatory version control.
3. Localized Deployment: Enables data privacy protection, offline availability, cost control, and flexible customization.

Safety Training

Provides employees with interactive regulatory learning, mock exams, and queries for specific job requirements.

Compliance Review

Checks the compliance of work processes, quickly locates clauses, and generates compliance reports.

Emergency Response

Quickly queries emergency procedures in case of accidents, provides regulatory basis, and records the response process.

Regulatory Update Tracking

Compares version differences, assesses impacts, and generates update recommendations.

Scalability

Can be adapted to other industry regulations (e.g., NR12 for mechanical safety), internal enterprise documents, and ISO/IEC international standards.

Challenge Solutions

• Professional terminology understanding: Domain model fine-tuning + terminology dictionary
• Clause relationships: Graph database for storing associations
• Local model performance: Quantization optimization + result caching
• Answer accuracy: Mandatory citation + multi-model validation

NR10-RAG-Assistant demonstrates the application potential of RAG technology in professional regulatory fields. It achieves accurate and private regulatory Q&A through hybrid search, local LLM, and domain adaptation, and has significant value for industries with high compliance requirements such as energy and chemical engineering.

Future directions: Multimodal support (drawing/photo understanding), enterprise system integration (ERP/CMMS), predictive compliance analysis, and VR training scenario support.