> ## Documentation Index
> Fetch the complete documentation index at: https://r2r-patch-fix-ingestion.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Configure your R2R retrieval pipeline

## 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.

## Key Configuration Areas

To configure the retrieval system in R2R, you'll need to focus on several areas in your `r2r.toml` file:

```toml
[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.

## 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

## Pipeline Architecture

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

```mermaid
graph TD
    A[User Query] --> B[RAG Pipeline]
    B --> C[Search Pipeline]
    B --> D[RAG Generation Pipeline]
    C --> E[Vector Search]
    C --> F[Knowledge Graph Search]
    E --> G[Search Results]
    F --> G
    G --> D
    D --> H[Generated Response]
```

## Next Steps

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

* [Ingestion Configuration](/documentation/configuration/ingestion/overview)
* [Vector Search Configuration](/documentation/configuration/retrieval/vector-search)
* [Knowledge Graph Search Configuration](/documentation/configuration/retrieval/knowledge-graph)
* [Retrieval Configuration](/documentation/configuration/retrieval/overview)