DAG-Orchestrated RAG Pipeline for Complex Query Reformulation
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Solution Overview
Problem
Traditional QA systems struggle to effectively handle complex or ambiguous queries, failing to accurately identify relevant information and generate satisfying responses.
Innovation Solution
A Retrieval Augmented Generation (RAG) based Query Reformulation Pipeline that leverages a Directed Acyclic Graph (DAG) structure for processing documents and knowledge graphs, involving retrieval, reranking, refinement, and summarization, with a Large Language Model (LLM) to generate responses grounded in a knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional QA systems are used to handle queries, then the system structure is simple, but the ability to handle complex or ambiguous queries effectively is poor
Solution Approach 1:
The query processing pipeline is segmented into distinct stages: retrieval stage (obtaining documents and knowledge graph triplets), reranking stage (assigning relevance scores), refinement stage (extracting additional information), and generation stage (forming refined queries and generating responses). This segmentation allows each component to specialize in specific tasks, improving overall system adaptability to complex queries while maintaining manageable complexity through modular design
Solution Approach 2:
A query reformulation component acts as an intermediary between the initial user query and the final response. This intermediary component reformulates the original query into a refined query that better captures the user's intent, enabling the system to handle complex and ambiguous queries more effectively by bridging the gap between simple user input and accurate information retrieval
2Measurement precision
If traditional QA systems are used, then the system is easier to operate, but the accuracy of identifying relevant information is insufficient
Solution Approach 1:
The system incorporates feedback mechanisms through the reranking stage, where relevance scores are assigned to retrieved documents and knowledge graph triplets based on their semantic similarity to the query. This feedback loop continuously refines the retrieval process, improving the accuracy of relevant information identification while operating automatically without user intervention
Solution Approach 2:
Traditional mechanical search methods are replaced with semantic similarity-based retrieval using knowledge graphs and document embeddings. This substitution enables more accurate identification of relevant information through meaning-based matching rather than keyword matching, maintaining ease of operation while significantly improving precision
3Adaptability or versatility
If traditional QA systems are used, then the response generation is simple, but the satisfaction of generated responses is insufficient
Solution Approach 1:
The system performs preliminary actions by retrieving, reranking, and refining information before generating the final response. The query reformulation component prepares refined queries that capture the full intent of complex questions, and the response generation component uses these preprocessed queries to produce high-quality responses that are more satisfying to users
Solution Approach 2:
The response generation process utilizes parameter changes by transforming the original query into a refined query with different semantic parameters that better represent user intent. This transformation enables the generation of more accurate and satisfying responses while the automated process maintains operational simplicity
Data Source
AI summary
A retrieval augmented generation (RAG) based query reformulation pipeline for a Query and Answer (QA) system is described. This pipeline leverages a Directed Acyclic Graph (DAG) and involves several operations, including retrieval of documents and knowledge graph triplets based on the initial query, reranking of retrieved elements based on relevance, refinement and summarization of relevant document chunks and knowledge triplets, reformulation of the initial query, and generation of a natural language response. The response is generated using a large language model (LLM) and is grounded in the knowledge base, which supports factual accuracy and consistency.