A Hybrid RAG System Integrating Knowledge Graph and Vector Retrieval: Based on Solution Technical Documents
Abstract
Abstract As enterprise environments increasingly manage large volumes of technical documentation across multiple heterogeneous sources, users face growing challenges in efficiently locating relevant information. This study proposes and implements a hybrid Retrieval-Augmented Generation (RAG) system that integrates a knowledge graph with vector retrieval to enhance information access in solution technical documents. The proposed system combines a Neo4j-based knowledge graph with a ChromaDB-based vector database, enabling both structural relationship exploration and semantic similarity search. A domain-specific knowledge graph is constructed by extracting entities and relationships from technical documents, including cross-document references derived from HTML anchor links. In parallel, vector embeddings are generated using a multilingual embedding model to support semantic retrieval across diverse document types. The system employs a hybrid ranking mechanism based on Reciprocal Rank Fusion (RRF) to effectively integrate graph-based and vector-based retrieval results. A seven-stage query processing pipeline is designed to analyze user queries, perform graph traversal and vector search, fuse results, and generate natural language responses using a large language model. Experimental evaluation on a multi-source technical document dataset demonstrates that the proposed hybrid approach significantly improves retrieval performance compared to single-method baselines, particularly…
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