Automatic Knowledge RAG with R2R

This is part two of Demystify Knowledge RAG frameworks. In our previous writing, we explored Knowledge RAG concepts, Microsoft GraphRAG data ingestion, and query capability. In this writing, we are going to continue exploring the use of SciPhi-AI/R2R, an open-source RAG engine, to perform automatic knowledge graph construction on file ingestion. Here, we explore R2R/GraphRAG capabilities and then take a look at a few approaches on converting unstructured text into structured data.

What is R2R?

RAG to Riches (R2R) is a tool that provides powerful document ingestion, search, and RAG capabilities with tone of useful features, including:

• Document Ingestion and Management
• LLM-Power Search (Vector, Hybrid and Knowledge Graph)
• User Management
• Observability and Analytics
• Dashboard UI

R2R offer Hypothetical Document Embedding (HyDE)- an advance techniques that significantly enhances RAG performance. By chaining multi-search pipelines result such as keyword search, vector search, similarity search into RAG for final response generation. Additionally, it support search ranking, local RAG, multiple LLM and advance RAG.

R2R / Local GraphRAG

in R2R Knowledge graph are created with local systems using the newly rleased Triplex Model.