About this video

• Video Title: HiRAG: Hierarchical Reasoning for GraphRAG (BEST RAG?)
• Channel: Discover AI
• Speakers: Not specified
• Duration: 21:57

Overview

This video introduces HiRAG (Hierarchical Reasoning for GraphRAG), a novel retrieval-augmented generation (RAG) system that utilizes a hierarchical knowledge graph structure to improve reasoning capabilities. The presenter explains the architecture and methodology of HiRAG, highlighting its advantages over traditional flat knowledge graphs and other RAG methods, particularly in handling complex, multi-layered information.

Key takeaways

• Hierarchical Knowledge Graph: HiRAG constructs a multi-layered knowledge graph where each layer represents a different level of abstraction or complexity, moving from specific entities to broader concepts.
• Hi-Index and Hi-Retrieve: The system consists of two main phases: Hi-Index for building the hierarchical knowledge graph and Hi-Retrieve for querying and extracting relevant information.
• LLM-Powered Summarization and Clustering: Large Language Models (LLMs) are used to extract entities, summarize information, and cluster nodes to create higher layers in the hierarchy.
• Community Detection: The Lightener algorithm is employed to detect communities across different layers, providing thematic groupings of related information.
• Fact-Based Reasoning: HiRAG aims to reduce hallucinations and provide fact-based answers by establishing clear reasoning paths and "bridge triplets" that connect different levels of abstraction.
• Improved Performance: The video suggests that HiRAG outperforms traditional RAG methods in benchmarks, positioning it as a potentially superior approach for complex information retrieval and generation tasks.

HiRAG has a two-phase architecture: Hi-Index and Hi-Retrieve.

Hi-Index (Offline Phase):

1. Document Ingestion & Chunking: Raw documents (PDFs, web pages, SQL data, etc.) are processed and broken down into smaller chunks.
2. Entity and Relation Extraction: LLMs extract entities and their relationships from the text chunks to build a base knowledge graph (G0). Embeddings are computed for all nodes.
3. Hierarchical Index Building:
   • Layer 0: Contains the base entities extracted from the text.
   • Clustering & Summarization: A clustering algorithm (e.g., Gaussian Mixture Model) is applied to Layer 0 entities to form thematic clusters. An LLM then summarizes these clusters, creating "summary nodes" for the next layer. Embeddings are updated.
   • Subsequent Layers: This process repeats, with each new layer summarizing clusters from the layer below. This continues until a stopping condition is met (e.g., a 5% difference threshold in cluster composition). These layers represent vertical abstraction.
4. Community Detection: The Lightener algorithm is applied across all layers of the hierarchical knowledge graph to identify overlapping communities, which are horizontal groupings of related thematic nodes. Each community may have an associated "global context" report.

Hi-Retrieve (Online Phase):

1. Query Embedding: The user's query is embedded.
2. Multi-Level Retrieval:
   • Local Entities: Relevant entities are selected from the top layer of the hierarchy, which encapsulates information from all lower levels. This can include fine-grained and abstract nodes.
   • Communities: Relevant communities are identified based on the query.
   • Global View: Information from the highest abstraction layer (summary nodes/reports) is accessed.
3. Shortest Path Calculation & Bridge Triplet Identification: The system calculates shortest paths between key entities within and across communities. "Bridge triplets" are identified, connecting local and global entities to form reasoning paths.
4. Context Assembly: The retrieved information (local snippets, community reports, bridge triplets) is assembled to create a comprehensive context for the LLM.
5. Answer Generation: An LLM uses the assembled context to generate a fact-based answer, leveraging the structured reasoning paths provided by HiRAG.