HyperGraphCAG

HyperGraphCAG (Hypergraph-based Retrieval Augmented Generation with Semantic Cache) is an advanced RAG framework that combines structured knowledge representation using hypergraphs with efficient response generation via semantic caching.

<img width="700" alt="Image" src="https://github.com/user-attachments/assets/8282b215-f3cd-4bac-86e2-0fcb21e43b66" />

⚙️ System Architecture

1. Knowledge Construction

Domain-specific documents are segmented into text chunks. A language model extracts entities and relations to build a knowledge hypergraph, where each hyperedge connects two or more entities through a descriptive relation.

2. Hypergraph-Guided Retrieval

At query time, a semantic search retrieves the most relevant entities and hyperedges from the hypergraph based on cosine similarity. These are expanded into a subgraph that captures the relevant context for the query.

3. Cache-Based Generation

The selected subgraph and its associated textual chunks are preprocessed and stored in a semantic cache. During inference, this cache is directly loaded into the model’s memory, enabling low-latency and cost-free response generation without external API calls.

🚀 Key Features

• Hypergraph-based structured knowledge representation
• Semantic search with graph-guided retrieval
• Efficient, cache-based generation with minimal latency
• Fully open-source and offline-compatible setup

📌 Future Work

Planned improvements include:

• Extending cache memory capacity to overcome LLM context limitations
• Enhancing entity and relation extraction accuracy
• Optimizing the cost and scalability of the cache construction phase

🧑‍💻 Author

Francesco Della Valle
Master's Thesis in Big Data Engineering
University of Naples "Federico II"
Academic Year 2024/2025