It is often effective to restructure data after ingestion to improve retrieval performance and accuracy. R2R supports knowledge graphs for data restructuring. You can find out more about creating knowledge graphs in the Knowledge Graphs Guide.

You can configure knowledge graph enrichment in the R2R configuration file. To do this, just set the kg.kg_enrichment_settings section in the configuration file. Following is the sample format from the example configuration file r2r.toml.

[kg]
provider = "neo4j"
batch_size = 256
kg_extraction_prompt = "graphrag_triplet_extraction_zero_shot"

  [kg.kg_creation_settings]
    entity_types = [] # if empty, all entities are extracted
    relation_types = [] # if empty, all relations are extracted
    fragment_merge_count = 4 # number of fragments to merge into a single extraction
    max_knowledge_triples = 100 # max number of triples to extract for each document chunk
    generation_config = { model = "gpt-4o-mini" } # and other generation params

  [kg.kg_enrichment_settings]
    max_description_input_length = 65536 # increase if you want more comprehensive descriptions
    max_summary_input_length = 65536
    generation_config = { model = "gpt-4o-mini" } # and other generation params
    leiden_params = { max_levels = 10 } # more params in graspologic/partition/leiden.py

  [kg.kg_search_config]
    model = "gpt-4o-mini"

Next you can do GraphRAG with the knowledge graph. Find out more about GraphRAG in the GraphRAG Guide.

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