Milvus powers Graph RAG systems that let AI agents traverse knowledge graphs through vector embeddings, chaining multiple retrieval steps for complex reasoning.
Graph RAG extends traditional RAG by treating knowledge as a network of entities and relationships. Instead of retrieving isolated chunks, agents need to follow edges through a graph—finding related entities, understanding connections, and reasoning across multiple hops. Milvus supports this by storing embeddings of both entities and relationships, enabling agents to start with an initial query, retrieve semantically similar entities, then use those entities as anchors for the next retrieval step. This chaining process allows agents to discover indirect relationships and perform multi-step reasoning without requiring pre-built graph traversal logic. For instance, an agent investigating fraud might start by finding suspicious transaction embeddings in Milvus, then use those results to retrieve associated customer profiles, and finally correlate with behavioral anomaly embeddings—all within the vector database’s semantic space. Milvus’s support for metadata filtering enhances Graph RAG by allowing agents to prune their search to specific relationship types or time windows. The database’s vector indexing algorithms (hierarchical navigable small world, IVF) ensure that even with thousands of entities and billions of relationships represented as embeddings, each retrieval hop completes in milliseconds. Teams can implement multi-hop reasoning by wrapping Milvus query calls in agent loop logic, using LangGraph or custom orchestration code to decide which retrieval steps to execute based on intermediate results.