PDF-Paper-AI-Agent: A Multi-Technology Integrated Intelligent Q&A System for Scientific Literature

PDF-Paper-AI-Agent is an open-source AI Agent designed to address the pain points of researchers when searching for information in massive academic PDF documents. It integrates technologies such as hybrid retrieval, GraphRAG, online learning, and Parameter-Efficient Fine-Tuning (PEFT/QLoRA) to enable accurate Q&A and page-level traceable citations, providing a lightweight solution for intelligent processing of scientific literature.

Traditional document Q&A systems have issues such as pure vector retrieval easily missing keywords, high deployment costs for large models, and lack of traceability in answers. This project adopts a "combination strategy" by integrating multiple complementary technologies to achieve professional-level Q&A quality while remaining lightweight.

1. Hybrid Retrieval System: Combines lexical retrieval (e.g., BM25) and dense vector retrieval to both accurately match professional terms and capture semantic similarity, improving recall rate; 2. GraphRAG Knowledge Graph Reasoning: Builds a document knowledge graph to support multi-hop reasoning, enabling answers to complex cross-document/chapter questions and adapting to the conceptual dependencies and citation relationships in scientific literature.

1. River Online Learning Feedback: Adjusts retrieval strategies and ranking weights in real-time based on user feedback to adapt to domain language habits and personalized needs; 2. PEFT/QLoRA Fine-Tuning: Eliminates the need to train the entire large model, instead fine-tuning small models via low-rank adapters, reducing memory requirements and enabling deployment on consumer-grade hardware.

Applicable to scenarios such as systematic literature reviews, quick location of experimental methods, cross-paper result comparison, and verification of original sources of conclusions. The page-level traceability function meets the strict citation standards of academic writing, ensuring answers can be traced back to specific positions in the original literature.

Hybrid retrieval solves the recall problem, GraphRAG handles complex reasoning, online learning enables personalization, and PEFT lowers deployment barriers. Each component reinforces the others: better retrieval provides materials for the graph, graph relationships improve retrieval relevance, and user feedback optimizes the overall process.

As an open-source project, it provides an extensible framework for intelligent literature processing, with a modular design supporting component replacement and discipline-specific customization. In the future, such multi-technology integrated Agent architectures are expected to become an important part of the scientific research toolbox.