An Adaptive Multi-Source Retrieval-Augmented Generation Framework Integrating Query Complexity Awareness and Confidence-Aware Fusion

Retrieval-Augmented Generation (RAG) has been observed to encounter challenges in heterogeneous query scenarios characterised by varying evidence requirements and reasoning depths. In order to address this limitation, the present paper puts forward a proposal for an Adaptive Multi-Source RAG framework (AMSRAG) that integrates query complexity awareness with confidence-aware fusion. The framework performs query complexity classification with a pretrained language model, calibrates the classification confidence to guide the dynamic scheduling of retrieval paths and the adjustment of fusion weights, and enables a controllable balance between answer quality and retrieval efficiency through hierarchical path selection and cross-source weighting. The experiments conducted on multiple open-domain question-answering datasets demonstrate that the query complexity classifier achieves an accuracy of 85.9% and a Macro-F1 score of 85.4%. These outcomes indicate the potential for the classifier to generate a reliable decision signal, which can subsequently be utilised to guide the process of adaptive retrieval and fusion. The proposed framework demonstrates a marked improvement in terms of both answer accuracy and retrieval relevance when compared to the fixed-pipeline RAG. In scenarios involving high-confidence queries, the system has been shown to effectively avoid redundant retrieval, thereby reducing the average number of retrievals. In instances of low-confidence complex queries, the system has been shown to enhance evidence coverage and completeness of answers through multi-source retrieval and confidence-weighted fusion. This study proposes a novel methodology for enhancing the adaptability and resource efficiency of RAG systems in response to heterogeneous query conditions.