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Introduction

Retrieval in R2R is a sophisticated system that leverages ingested data to provide powerful search and Retrieval-Augmented Generation (RAG) capabilities. It combines vector-based semantic search, knowledge graph querying, and language model generation to deliver accurate and contextually relevant results.

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Key Configuration Areas

To configure the retrieval system in R2R, you’ll need to focus on several areas in your r2r.toml file: 
[database]
provider = "postgres"

[embedding]
provider = "litellm"
base_model = "openai/text-embedding-3-small"
base_dimension = 512
batch_size = 128
add_title_as_prefix = false
rerank_model = "None"
concurrent_request_limit = 256

[kg]
provider = "postgres"
batch_size = 256

[completion]
provider = "litellm"
concurrent_request_limit = 16

[completion.generation_config]
model = "openai/gpt-4"
temperature = 0.1
top_p = 1
max_tokens_to_sample = 1_024
stream = false
These settings directly impact how R2R performs retrieval operations:
  • The [database] section configures the vector database used for semantic search and document management.
  • The [embedding] section defines the model and parameters for converting text into vector embeddings.
  • The [kg] section, when configured, enables knowledge graph-based retrieval.
  • The [completion] section sets up the language model used for generating responses in the RAG pipeline.

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Customization and Advanced Features

R2R’s retrieval system is highly customizable, allowing you to:
  • Implement hybrid search combining vector-based and knowledge graph queries
  • Customize search filters, limits, and query generation
  • Add custom pipes to the search and RAG pipelines
  • Implement reranking for improved result relevance

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Pipeline Architecture

Retrieval in R2R is implemented as a pipeline and consists of the main components shown below:

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Next Steps

For more detailed information on configuring specific components of the ingestion pipeline, please refer to the following pages: