Ensemble Knowledge Extraction for Accurate RAG Query Context
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Solution Overview
Problem
Existing QA systems struggle to accurately handle complex or ambiguous queries, failing to identify relevant information and generate satisfying responses due to inefficient information retrieval and integration across multiple documents.
Innovation Solution
Implementing multiple knowledge extraction pipelines to embed vectors with metadata, generate knowledge graphs, and create recursive hierarchical trees, which are combined to augment user queries and enhance LLM functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple knowledge extraction pipelines are implemented to improve query processing accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system divides the knowledge extraction process into multiple specialized pipelines, each handling specific aspects of information retrieval. These pipelines include entity extraction, relationship extraction, event extraction, and attribute extraction, allowing complex query processing to be broken down into manageable, specialized components that can be executed independently and then integrated.
Solution Approach 2:
Multiple knowledge extraction pipelines are merged into a unified system that processes queries through parallel extraction pathways. The results from entity extraction, relationship extraction, event extraction, and attribute extraction pipelines are combined and integrated to form comprehensive query responses, leveraging the strengths of each individual pipeline while maintaining system coherence.
2Loss of information
If multiple knowledge extraction pipelines are used to enhance contextual understanding, then information retrieval completeness improves, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing knowledge data into structured formats before actual query processing. Knowledge graphs, entity databases, and relationship repositories are prepared in advance, allowing the multiple extraction pipelines to quickly retrieve and process information without redundant computation during query execution.
Solution Approach 2:
The multiple knowledge extraction pipelines operate continuously and parallelly during query processing, with each pipeline working simultaneously on different aspects of the query. This continuous parallel processing ensures that all relevant information is extracted without interruption, maintaining complete information retrieval while minimizing total processing time through efficient resource utilization.
Data Source
AI summary
Methods, systems, apparatuses, devices, and computer program products are described. A system may obtain a set of documents associated with a knowledge base for retrieval-augmented generation (RAG). The system may generate multiple representations of the information included in the documents using multiple knowledge extraction pipelines. For example, the system may generate a set of metadata-based vector embeddings based on the documents, a set of knowledge graphs based on the documents, and a set of hierarchical tree representations based on the documents. The system may receive a user query and may retrieve contextual information from the set of vector embeddings, the set of knowledge graphs, and the set of hierarchical tree representations to augment the user query for a large language model (LLM) prompt. The system may input the prompt to the LLM, and the LLM may output a response based on the user query and the contextual information.