Battle of the Semantics: GraphRag vs Embeddings Index

Comparing RAG over a rich and complex text using GraphRag vs traditional embeddings index.

Standard Retrieval Augmented Generation (RAG) often relies on chunking text and retrieving relevant pieces using embedding similarity. While effective for many use cases, this approach can struggle when a deeper, more nuanced understanding of the source text is required for accurate generation.

Microsoft’s GraphRag offers an alternative indexing method. Instead of just embeddings, it uses an LLM to build a knowledge graph of entities and relationships within the source text, aiming for a richer semantic representation. Does this graph-based approach yield superior context retrieval compared to traditional embeddings?

github.com/intellectronica/battle-of-the-semantics tackles this question directly. It provides a practical comparison between GraphRag and standard embedding techniques for indexing and retrieval.

  • Direct Comparison: Explore the battle-of-the-semantics.ipynb Jupyter Notebook, which details the setup and results of the comparison.
  • Understand the Trade-offs: Gain insights into scenarios where GraphRag’s deeper semantic analysis might outperform embedding similarity, and vice-versa.

For developers and AI practitioners evaluating advanced RAG strategies, particularly for complex information retrieval tasks, this repository offers valuable comparative insights.

  • Explore the GraphRag vs. Embeddings comparison on GitHub to examine the findings:
  • Watch the video for a detailed discussion and demo:Play