{"repo":"OpenDFM/NeuSym-RAG","free":true,"listed":false,"github":"https://github.com/OpenDFM/NeuSym-RAG","clone":"git clone https://github.com/OpenDFM/NeuSym-RAG.git","description":"[ACL 2025] NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering","language":"Python","stars":28,"topics":["academic-research","database","neural-symbolic-processing","neural-symbolic-reasoning","question-answering","rag","retrieval-augmented-generation","text-to-sql","vectorstore"],"license":null,"category":"ai-agents","readme_excerpt":"NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering ACL 2025 (Main) 💫 Table of Contents (Click to expand) - 💡 Main Contributions - 🔍 Quick Start - 📖 PDF Parsing and Encoding - 📊 Experiment Results - 📚 Detailed Documents and Tutorials - ✍🏻 Citation 💡 Main Contributions - We are the first to integrate both vector-based neural retrieval and SQL-based symbolic retrieval into a unified and interactive NeuSym-RAG framework through executable actions. - We incorporate multiple views for parsing and vectorizing PDF documents, and adopt a structured database schema to systematically organize both text tokens and encoded vectors. - Experiments on three realistic full PDF-based QA datasets w.r.t. academic research (AirQA-Real, M3SciQA and SciDQA) validate the superiority over various neural and symbolic baselines. 🔍 Quick Start 1. Create the conda environment and install dependencies: - Install poppler on your system - Follow the Official Guide to install MinerU based on your OS platform - Check our TroubleShooting tips to ensure the installation of MinerU is successful - Install other pip requirements 2. Prepare the following models for vector encoding: - sentence-transformers/all-MiniLM-L6-v2 - BAAI/bge-large-en-v1.5 - openai/clip-vit-base-patch32 - For embedding model customization, refer to vectorstore doc 3. Download the dataset-related files into the folder data/dataset 👉🏻 HuggingFace - AirQA-Real : this work, including the","default_branch":null,"files":null,"tree":[],"storefront":"/r/OpenDFM","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/OpenDFM/NeuSym-RAG/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}