Explore a comprehensive discussion on integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction in this MLOps.community reading group session. Dive into the paper "HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction" as experts analyze its approach to combining knowledge graphs with vector-based retrieval for improved financial data extraction. Learn about context-mixing methods, graph pruning techniques, and the potential of hybrid models in enhancing information retrieval from unstructured financial documents. Gain insights on faithfulness metrics, text preprocessing, graph algorithms, and the application of graph neural networks in re-ranking and retrieval processes. Understand the challenges and opportunities in making larger language models traverse graph structures and the importance of separating entity attributes from relationships.
Integrating Knowledge Graphs and Vector RAG for Efficient Information Extraction - Reading Group Session
Overview
Syllabus
[] Hybrid rank combines knowledge graphs, vector retrieval
[] Hybrid drag improves accuracy using knowledge graphs
[] Text preprocessing and graph algorithms improve LLM responses
[] Faithfulness measures the consistency of LLM-generated answers
[] Faithfulness metric should normalize statements' word counts
[] Analyzed Nifty 50 earning calls for hedge fund
[] Construct a knowledge graph and chunk text data
[] Example of earnings extraction illustrating graph advantages
[] Traversing graph to find relevant entities' relationships
[] Highly connected graph; combine vectors, graphs arbitrarily
[] Graph neural networks improve re-ranking and retrieval
[] Comparison methods are unfair due to unequal context size
[] Making larger language models traverse graph structures
[] Consider separating entity attributes from relationships
[] Join discussions on Slack, suggest topics, participate
[] Wrap up
Taught by
MLOps.community
