Knowledge Graph Extraction for Complex RAG Reasoning
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Solution Overview
Problem
Existing retrieval augmented generation (RAG) techniques struggle with intricate analytical tasks and require user knowledge to determine queries, failing to provide accurate aggregate reasoning across complex questions, especially in unfamiliar and rapidly evolving data sources.
Innovation Solution
The implementation of a system that leverages large language models (LLMs) to construct and traverse knowledge graphs, enabling the extraction of key relational information and providing comprehensive responses to complex queries by navigating the graph structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional RAG techniques are used for similarity searches over embeddings, then the system can answer simple domain-specific questions, but it fails at intricate analytical tasks and requires user knowledge to determine queries
Solution Approach 1:
The patent introduces an LLM-based query reformulation intermediary that automatically translates user questions into optimized search queries. This intermediary layer eliminates the need for users to have domain-specific knowledge while maintaining the ability to handle complex analytical tasks through intelligent query transformation and knowledge graph traversal.
Solution Approach 2:
The system dynamically changes the parameters of query processing by switching between different search strategies (semantic search, knowledge graph traversal, hybrid approaches) based on the complexity and type of the analytical task. This allows the system to adapt its operation mode to match the required level of analysis without requiring user intervention.
2Measurement precision
If RAG performs similarity searches over embeddings in vector databases, then it can retrieve relevant information, but it fails to provide accurate aggregate reasoning across complex questions
Solution Approach 1:
The patent segments the information retrieval process into multiple specialized components: semantic search for simple queries, knowledge graph construction for relational data, and LLM-based reasoning for complex analysis. Each segment handles specific types of tasks with high precision, and their results are aggregated to provide comprehensive answers to complex questions.
Solution Approach 2:
The system creates a composite retrieval architecture that combines vector database embeddings, knowledge graphs, and LLM reasoning capabilities. This composite approach leverages the strengths of each component to achieve accurate aggregate reasoning across complex questions that would be impossible to answer using any single method alone.
3Adaptability or versatility
If LLM reasoning frameworks like Chain-of-Thought are used with RAG, then multi-hop reasoning questions can be answered, but the system still fails at intricate analytical tasks and is heavily dependent on user knowledge
Solution Approach 1:
The system implements self-service by automatically performing query reformulation, knowledge graph construction, and reasoning task decomposition without requiring user knowledge. The LLM frameworks operate autonomously to translate user questions into multi-step reasoning processes, eliminating the need for users to understand the underlying complexity or formulate specialized queries.
Data Source
AI summary
This document relates to providing meaningful information relating to a dataset. One example can obtain aggregated summaries and a related knowledge graph. The example can enable local, community, and global retrieval augmented generation utilizing the aggregated summaries and the knowledge graph.