Overview
Configure your R2R knowledge graph provider.
Knowledge Graph Provider
R2R supports knowledge graph functionality to enhance document understanding and retrieval. By default, R2R creates the graph by clustering with graspologic
and saving the output triples and relationships into Postgres. We are actively working to integrate with Memgraph. You can find out more about creating knowledge graphs in the GraphRAG Cookbook.
To configure the knowledge graph settings for your project, edit the kg
section in your r2r.toml
file:
[kg]
provider = "postgres"
batch_size = 256
kg_triples_extraction_prompt = "graphrag_triples_extraction_few_shot"
[kg.kg_creation_settings]
entity_types = [] # if empty, all entities are extracted
relation_types = [] # if empty, all relations are extracted
generation_config = { model = "openai/gpt-4o-mini" }
max_knowledge_triples = 100 # max number of triples to extract for each document chunk
fragment_merge_count = 4 # number of fragments to merge into a single extraction
[kg.kg_enrichment_settings]
max_description_input_length = 65536 # increase if you want more comprehensive descriptions
max_summary_input_length = 65536
generation_config = { model = "openai/gpt-4o-mini" } # and other generation params below
leiden_params = {}
[kg.kg_search_settings]
generation_config = { model = "openai/gpt-4o-mini" }
Let’s break down the knowledge graph configuration options:
provider
: Specifies the knowledge graph provider. Currently, “postgres” is supported.batch_size
: Determines the number of entities or relationships to process in a single batch during import operations.kg_triples_extraction_prompt
: Specifies the prompt template to use for extracting knowledge graph information from text.kg_creation_settings
: Configuration for the model used in knowledge graph creation.max_knowledge_triples
: The maximum number of knowledge triples to extract for each document chunk.fragment_merge_count
: The number of fragments to merge into a single extraction.generation_config
: Configuration for the model used in knowledge graph creation.
kg_enrichment_settings
: Similar configuration for the model used in knowledge graph enrichment.generation_config
: Configuration for the model used in knowledge graph enrichment.leiden_params
: Parameters for the Leiden algorithm.
kg_search_settings
: Similar configuration for the model used in knowledge graph search operations.
Setting configuration values in the r2r.toml
will override environment variables by default.
Knowledge Graph Operations
- Entity Management: Add, update, and retrieve entities in the knowledge graph.
- Relationship Management: Create and query relationships between entities.
- Batch Import: Efficiently import large amounts of data using batched operations.
- Vector Search: Perform similarity searches on entity embeddings.
- Community Detection: Identify and manage communities within the graph.
Customization
You can customize the knowledge graph extraction and search processes by modifying the kg_triples_extraction_prompt
and adjusting the model configurations in kg_extraction_settings
and kg_search_settings
. Moreover, you can customize the LLM models used in various parts of the knowledge graph creation process. All of these options can be selected at runtime, with the only exception being the specified database provider. For more details, refer to the knowledge graph settings in the search API.
By leveraging the knowledge graph capabilities, you can enhance R2R’s understanding of document relationships and improve the quality of search and retrieval operations.
Next Steps
For more detailed information on configuring specific components of the ingestion pipeline, please refer to the following pages:
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