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Settings

The Graph RAG approach of TrustGraph is to perform a semantic similarity search using the request. The request finds the most relevant mapped vector embeddings to return a list of nodes stored in the knowledge graph. Subgraphs are then generated based on that list of entities. The subgraphs are the input context for the Graph RAG responses. The subgraphs can be adjusted with the following parameters:

  • entity-limit
  • triple-limit
  • max-subgraph-size

Entity Limit​

Specifies the number of entities to return based on the semantic similiarity search of the vector embeddings.

Triple Limit​

For each entity, specifies the number of triples to return from the knowledge graph.

Max Subgraph Size​

The total returned knowledge graph subgraph is the product of entity-limit and triple-limit. The max-subgraph-size parameter can provide a cap to the possible returned subgraph. If the returned subgraph size exceeds max-subgraph-size, some triples will be discarded.

LLM Input Context​

With dense knowledge graphs, the returned subgraphs can be quite large. With the default settings, the subgraph can easily exceed 10,000 tokens. While long context LLMs can handle this amount of tokens, smaller models may struggle. It's difficult to predict, but the total subgraph size tends to generate 3x-5x the number of tokens. For instance, if the max-subgraph-size is 1000, it's likely the subgraph will be 3,000-5,000 tokens.

Container Settings​

These settings can be adjusted in the command list of the graph-rag container found in the configuration YAML file. The default settings are:

- --entity-limit
- '50'
- --triple-limit
- '30'
- --max-subgraph-size
- '3000'