The LightRAG system is a hybrid retrieval model that combines the strengths of both graph-based reasoning and semantic search. It uses a dual-level approach, where one path focuses on relationships (high-level keywords) and the other on entities (low-level keywords). Both paths leverage vector similarity to surface semantically relevant content and are enriched with graph traversal and metadata. The system retrieves structured knowledge from a graph and surrounding documents using keyword-based retrieval, while also running a semantic vector search in parallel. The final output is combined and merged into a single CSV-formatted string block for the LLM to generate an accurate and contextually grounded response. LightRAG offers practical benefits such as flexible design, easy addition of new information without rebuilding everything, making it particularly valuable for businesses with constantly evolving knowledge.