Graph-Based RAG for Manuscript Collections: A LangGraph Approach
Abstract
Abstract This paper introduces a conversational agent designed for querying digitized historical manuscript collections, developed as part of the MAGIC project. The system incorporates hybrid sparse and dense retrieval, a Neo4j knowledge graph, ALTO XML-based visual grounding, and a multi-step LangGraph ReAct agent powered by a Llama-3.3-70B backend. To identify the most effective retrieval strategy, a benchmark of 100 hand-annotated queries was constructed, covering six query types across two 15th-century incunabula. Multiple retrieval methods were evaluated, including BM25, dense retrieval, hybrid Reciprocal Rank Fusion (RRF), cross-encoder reranking, graph-augmented retrieval, and Hypothetical Document Embeddings (HyDE), using standard information retrieval metrics and latency measurements. The results indicate that Hybrid RRF achieves the most favorable precision–latency trade-off and is positioned on the Pareto frontier for interactive applications. However, no single method demonstrates optimal performance across all query types. Graph-based expansion substantially enhances catalog and complex queries that require relational reasoning, but reduces effectiveness for semantic and entity queries. This outcome supports the adoption of a query-adaptive retrieval strategy within the agent. Furthermore, HyDE consistently underperforms on historical text due to temporal distribution mismatch, resulting in increased latency without corresponding improvements in retrieval…
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