Topic: Graph Rag - an LLM derived Knowledge Graph for Retrieval-Augmented Generation (RAG).
Teaser: Example from 'Behind the Tech' podcast will be discussed later.
Overview of Graph Rag
Definition: Graph Rag consists of a two-step process that includes indexing private data to create LLM-derived knowledge graphs and orchestration for better retrieval.
Key Components:
Indexing Process: Creates knowledge graphs that serve as LLM memory representations.
Orchestration Mechanism: Utilizes pre-built indices for enhanced RAG operations.
Key Differentiators of Graph Rag
Enhanced Search Relevancy: Provides a holistic view of data semantics.
New Analytical Scenarios: Enables scenarios requiring large context for data analysis, trend summarization, etc.
Baseline RAG vs. Graph Rag
Baseline RAG Process:
Chunk private datasets using embeddings.
Store in vector databases.
Perform nearest neighbor searches.
Graph Rag Process:
Analyzes text chunks and performs reasoning over sentences with LLM in a single pass.
Focuses on relationships and their strengths between entities rather than just named entities.
Knowledge Graph Creation
Entity Recognition: Named entity recognition enhanced by understanding relationships' semantics.
Weighted Relationships: Graphs can be generated which reflect richer semantics than traditional networks.
Graph Machine Learning: Allows semantic aggregations and the creation of granular filters for querying.
Use Cases for Graph Rag
Dataset question generation
Summarization and Q&A
Various analytical applications
Demonstration of Graph Rag
Data Set: 3,000 articles on the Russian-Ukrainian conflict.
Question Example: "What is Novorossiya and what are its targets?"
Baseline RAG: Failed to answer fully.
Improved RAG: Partially answered but lacked specifics on targets.
Graph Rag: Successfully identified specific targets with evidence and context.
Verification and Hallucination Reduction
Can evaluate generated responses for accuracy and grounding with independent verification agents.
Thematic Analysis with Graph Rag
Question Example: "What are the top five themes in the data?"
Baseline RAG: Inadequate thematic analysis.
Graph Rag: Provided accurate themes focused on the conflict.
Performance Metrics: Graph Rag used 50,000 tokens and took 71 seconds, but provided richer and correct answers.
Network Map Visualization
Visual representation of the knowledge graph of the dataset.
Identifies communities of related entities (e.g., soccer topics alongside war topics).
Additional Data Set Analysis
Kevin's Podcast Transcripts:
Utilized Graph Rag to analyze technology trends and topics discussed.
Demonstrated the capability of grouping episodes semantically.
Conclusion
Graph Rag represents a significant advancement in retrieval-augmented generation by providing deeper semantic understanding and better analytical capabilities.