Indexing Architecture

Key Concepts

Knowledge Model

In order to support the GraphRAG system, the outputs of the indexing engine (in the Default Configuration Mode) are aligned to a knowledge model we call the GraphRAG Knowledge Model. This model is designed to be an abstraction over the underlying data storage technology, and to provide a common interface for the GraphRAG system to interact with. In normal use-cases the outputs of the GraphRAG Indexer would be loaded into a database system, and the GraphRAG's Query Engine would interact with the database using the knowledge model data-store types.

Workflows

Because of the complexity of our data indexing tasks, we needed to be able to express our data pipeline as series of multiple, interdependent workflows.

LLM Caching

The GraphRAG library was designed with LLM interactions in mind, and a common setback when working with LLM APIs is various errors due to network latency, throttling, etc.. Because of these potential error cases, we've added a cache layer around LLM interactions. When completion requests are made using the same input set (prompt and tuning parameters), we return a cached result if one exists. This allows our indexer to be more resilient to network issues, to act idempotently, and to provide a more efficient end-user experience.