GraphRAG systems are transforming how we handle unstructured data, but indexing costs can grow as datasets grow.

We’ve been tackling these challenges head-on. Here’s what we’ve learned and built:

• Composite Indexing: By combining multiple properties into single indexes, we’ve reduced query latency and memory overhead without sacrificing flexibility.
• Cardinality Reduction: Eliminating duplicates early in graph traversals has cut downstream LLM token usage, optimizing both cost and performance.
• Optimized Property Access: Deferring property access and caching embeddings improved query speeds by up to 28x in large-scale workloads.

We also integrated techniques like Wind-Bell Indexing and trie-based subgraph matching to streamline path queries and subgraph detection. These strategies enable scalable, low-latency operations even in dense graphs.

Learn more here: Reducing GraphRAG Indexing Costs