Indexes are the unsung heroes of GraphRAG. Without them, every retrieval is a costly traversal or vector search.
There are typically three classes of indexes that power an efficient GraphRAG system:
1 ) Text Index for text contents, including B-tree text index for exact match, and full text index for complex, content-based text searches, especially in large datasets.
2 ) Vector Index for embeddings, i.e. vectors encoded from raw data of either text or image. It maps text, embeddings, or image features to nodes for semantic similarity search. Example implementations are pgvector, Qdrant, Milvus.
3 ) Structual Index allows the graph engine to quickly locate nodes, edges, and their relationships without scanning the entire graph. Different types of graph database have their specific implementations over graph patterns.
A practical architecture usually integrates more than one indices (for unstructured context retrieval) and a graph database (for structure-aware traversal).
The challenge for data engineers is keeping them synchronized — when a node or document is updated, both embeddings and graph structure must be refreshed.
In one of my earlier posts shared below, I demonstrated the process of combing both vector match with graph traversals in Neo4j.
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