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 in complex queries requiring cross-document reasoning. The results indicate that integrating knowledge graph and vector retrieval within a hybrid RAG framework provides an effective solution for enhancing information retrieval in enterprise technical documentation environments.