This guide explains how to build and use knowledge graphs with R2R. It covers setup, basic example, construction, navigation, querying, visualization, and advanced examples.
R2R (RAG to Riches) is a platform designed to help developers build, scale, and manage user-facing Retrieval-Augmented Generation (RAG) applications. It bridges the gap between experimentation and deployment of state-of-the-art RAG applications by offering a complete platform with a containerized RESTful API. The platform includes features like multimodal ingestion, hybrid search, GraphRAG, user and document management, and observability/analytics.
#### Key Features
- **Multimodal Ingestion:** Supports a wide range of file types including .txt, .pdf, .json, .png, .mp3, and more.
- **Hybrid Search:** Combines semantic and keyword search with reciprocal rank fusion for improved relevancy.
- **Graph RAG:** Automatically extracts relationships and constructs knowledge graphs.
- **App Management:** Efficient management of documents and users with full authentication.
- **Observability:** Allows performance analysis and observation of the RAG engine.
- **Configurable:** Uses intuitive configuration files for application provisioning.
- **Application:** Includes an open-source React+Next.js app with optional authentication for GUI interaction.
#### Key Features
- **Multimodal Ingestion:** Supports a wide range of file types including .txt, .pdf, .json, .png, .mp3, and more.
- **Hybrid Search:** Combines semantic and keyword search with reciprocal rank fusion for improved relevancy.
- **Graph RAG:** Automatically extracts relationships and constructs knowledge graphs.
- **App Management:** Efficient management of documents and users with full authentication.
- **Observability:** Allows performance analysis and observation of the RAG engine.
- **Configurable:** Uses intuitive configuration files for application provisioning.
- **Application:** Includes an open-source React+Next.js app with optional authentication for GUI interaction.
