In an era of rapidly evolving international relations, understanding the complexities of foreign policy requires more than just raw data. Context, historical insight, and the ability to make connections across intricate geopolitical factors are necessary to develop foreign policy. This AI-powered Foreign Policy Assistant enhances decision-making by combining knowledge graphs with large language models (LLMs) to provide nuanced, context-aware responses to assist policymakers in foreign policy decision-making.

Why is This Important?

Simple text-based retrieval often lacks the depth and necessary structure to reflect real-world international relationships, such as treaties, trade agreements, and geopolitical conflicts. By using a GraphRAG approach—which combines knowledge graphs with Retrieval-Augmented Generation (RAG)—this system offers:

• More Informed Analysis: Connects structured and unstructured data for deeper insights.
• Enhanced Contextual Understanding: Captures complex relationships between nations, events, and policies.
• Reliable Decision Support: Helps policymakers and researchers analyze vast amounts of data to inform important decisions.

Key Features

• GraphRAG System: Merges knowledge graphs with LLMs for more intelligent and context-aware retrieval and generation.
• Foreign Policy Focus: Designed specifically for high-level decision-making, ensuring multi-perspective and global insights.
• Neo4j Knowledge Graph Integration: Stores structured data on treaties, trade agreements, historical events, and diplomatic relations.

System Architecture

• Neo4j Knowledge Graph: A structured representation of foreign policy data, enabling efficient querying of key relationships.
• LLM Integration: The AI model interacts with both structured (knowledge graph) and unstructured (text-based) data to generate intricate, well-informed responses.
• GraphRAG Framework: Incorporating graph-based data alongside traditional RAG for enhanced accuracy and contextual depth.

Research Paper & Implementation

This project is supported by an in-depth research paper that outlines its methodology, objectives, and key findings. The paper is available in PDF format within the GitHub repository