Reducing GraphRAG Indexing Costs

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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

Hands-on debugging session: instrument, monitor, and fix

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