How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
This is the code for GNN-RAG: Graph Neural Retrieval for Large Language Modeling Reasoning.
The main features of cmavro/gnn-rag are: Advanced RAG Techniques.
Open-source alternatives to cmavro/gnn-rag include: ayanami0730/arag — If you find our project helpful, please give us a star ⭐ on GitHub! ekimetrics/adaptive-chunking — Selecting the Best Chunking Strategy per Document for RAG. microsoft/graphrag — GraphRAG is a data processing pipeline and retrieval engine designed to transform unstructured text into… vectifyai/pageindex — PageIndex is an agent-ready knowledge engine that processes documents into hierarchical tree structures to enable… vitali87/code-graph-rag. weizhepei/instructrag — InstructRAG.
If you find our project helpful, please give us a star ⭐ on GitHub!
Selecting the Best Chunking Strategy per Document for RAG
GraphRAG is a data processing pipeline and retrieval engine designed to transform unstructured text into interconnected knowledge graphs. By utilizing language models to extract entities and relationships, it builds structured representations of information that enable context-aware retrieval for downstream applications. The system distinguishes itself through hierarchical graph clustering and large-scale data synthesis, which organize massive document corpora into multi-level structures. This approach allows for both vector-based semantic searches and graph-based traversals, providing a comp
PageIndex is an agent-ready knowledge engine that processes documents into hierarchical tree structures to enable reasoning-based information retrieval. By organizing content into logical trees rather than relying on traditional vector database chunking, the platform preserves the original structure and flow of complex documents. It functions as a Model Context Protocol server, allowing external AI agents to connect to and query indexed knowledge bases through standardized communication protocols. The platform distinguishes itself by using vision-language models to process raw document images