Context Graphs vs. Vector Search: When RAG Falls Short

What this post added

This post introduces context graphs as a complementary retrieval mechanism to vector search for RAG pipelines. It explains the limitations of vector-only RAG, such as missing connected information and issues with multi-hop questions, chunk boundaries, and exact matches. It details the architecture of context graphs (entity/relationship extraction, knowledge graph construction, graph-aware retrieval) and highlights their strengths in multi-hop question answering, technical specification Q&A, and global/thematic queries. It also contrasts these with scenarios where vector search excels (single-hop lookups, self-contained content) and discusses the costs of graph construction and retrieval latency. The post advocates for dual-channel retrieval, combining vector and graph search, and positions Redis Query Engine's hybrid search and Redis Iris's unified context/memory layer as foundational components for such architectures.