Winest-Nigeria/LLM-KnowledgeBase

# 🌐 TechRangers AI-Native Standards Support System ### FG-AINN-I Build-a-thon 2025 Submission ## 📖 Overview This project presents an **AI-native, multi-agent orchestration system** developed by **Team TechRangers** for the **FG-AINN-I Build-a-thon 2025**. Our system is designed to **bridge the standardization gap** in 6G telecommunications by enabling automated gap detection and contribution support for underrepresented regions—particularly across Africa. Our system builds a transparent, intelligent, and semantically enriched infrastructure for standards contribution using autonomous agents, a fine-tuned Large Language Model (LLM), and a multimodal knowledge base powered by vector embeddings and semantic search. --- ## 🎯 Objectives - Enable inclusive and region-aware participation in 6G standardization. - Identify gaps in ITU-T 6G standards using AI-native methods. - Provide a scalable, autonomous, and user-friendly environment for new and existing contributors. - Drastically reduce technical and procedural entry barriers. --- ## 🧩 System Architecture & Components ### 🔍 AI-Powered Multimodal Knowledge Base - Ingests ITU-T standard documents across formats: **text, tables, images, and audio**. - Clause-level segmentation and metadata tagging (e.g., *region*, *energy*, *rural*). - Stored in a **Pinecone** vector database for **semantic and hybrid search**. - Automatically updated via **OCR**, **transcription**, and **embedding refresh** pipelines. --- ### 🧠 Multi-Agent Architecture (Built with LangChain) | Agent Name | Role | |----------------|----------------------------------------------------------------------| | **Cartographer** | Performs topic modeling and document summarization. | | **Analyst** | Performs clause comparison to detect outdated or missing content. | | **Hypothesiser** | Proposes testable questions reg …