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📦 오픈소스
#ai
#ai 모델
#gemini
#llama
#openai
#rag
#법률
#싱가포르
#정보검색
원문 출처: hackernews · Genesis Park에서 요약 및 분석
요약
싱가포르의 법률, 역사 및 인프라 정보를 제공하는 '싱가포르 인텔리전스 RAG 시스템'이 공개되었습니다. 이 프로젝트는 LLM의 환각 현상을 방지하기 위해 3만 3,000페이지 이상의 문서를 기반으로 RAG(검색 증강 생성) 파이프라인과 FAISS 벡터 데이터베이스를 활용하여 사실 기반의 답변을 생성합니다. 또한 99.9% 가동 시간을 보장하는 3중 AI 장애 조치 백엔드와 글래스모피즘이 적용된 인터랙티브 UI를 특징으로 하며, Hugging Face Spaces를 통해 배포되었습니다.
본문
The Singapore Intelligence RAG System is an intelligent platform that utilizes AI technology to deliver accurate and relevant information about the legal system, policies, and historical events of Singapore, as well as its critical infrastructure. Unlike other LLMs, which have the tendency to "hallucinate" facts, the Singapore Intelligence RAG System employs Retrieval-Augmented Generation (RAG). It relies on a carefully curated set of Singaporean data (more than 33,000 pages of PDFs) to ensure that all answers are based on factual reality. The system follows a high-performance RAG pipeline optimized for low-resource environments: - Ingestion: Processed 33,000+ pages of Singaporean legal and historical documents. - Vectorization: Used BGE-M3 to create 1024-dimensional semantic embeddings. - Retrieval: Implemented FAISS (Facebook AI Similarity Search) for millisecond-latency vector lookups. - Generation: A "Triple-Failover" logic ensures 99.9% uptime. For reliability in demos and heavy traffic, the system establishes a robust chain of command for LLM inference as follows: The frontend interface is a custom-built Framer Code Component (React + Framer Motion). - Glassmorphism: Real-time backdrop blur ( backdrop-filter: blur(25px) ). - Spring Physics: Smooth sideways expansion on hover. - Minimalist Design: SVG iconography and San Francisco typography. Rather than using API calls for vectorization (which incurs latency and expense), the embedding model is executed locally within the application container for privacy and performance. | Component | Technology | Description | |---|---|---| | Frontend | React, Framer Motion | Interactive "Ask AI" widget. | | Backend | Flask, Gunicorn | REST API handling RAG logic. | | Vector DB | FAISS (CPU) | Local, high-speed similarity search. | | Embeddings | Sentence-Transformers | BGE-M3 (Local(server based)). | | LLMs | Gemini 2.5 flash, Llama 3.3 | Text generation and synthesis. | | Deployment | Hugging Face Spaces | Docker-based cloud hosting. | - flask - flask-cors - python-dotenv - google-generativeai - google-genai - langchain - langchain-google-genai - langchain-community - langchain-huggingface - faiss-cpu - sentence-transformers - pypdf - tiktoken - numpy - gunicorn - setuptools - wheel - scikit-learn - openai git clone [git clone https://github.com/adityaprasad-sudo/Explore-Singapore.git)
Genesis Park 편집팀이 AI를 활용하여 작성한 분석입니다. 원문은 출처 링크를 통해 확인할 수 있습니다.
공유