PangHu1020

PangHu1020 / scholar-rag

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A beginner-friendly and extensible Agentic RAG project that demonstrates the full pipeline of document parsing, retrieval, reranking, workflow orchestration, tool calling, and answer generation, designed for both learning and secondary development.

11
0
100% credibility
Found Apr 02, 2026 at 11 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

ScholarRAG is a chat interface for uploading academic PDF papers and asking natural language questions to receive grounded answers with citations.

How It Works

1
🔍 Discover ScholarRAG

You find this friendly tool that helps everyday people chat with their academic papers to get clear answers.

2
🚀 Start the chat app

Follow simple steps to get the web chat running on your computer in minutes.

3
📤 Upload your papers

Drag and drop PDF files into the app, and watch them get ready for questions.

4
💬 Ask natural questions

Type any question about the papers, like 'What are the main results?' and hit send.

5
💡 Receive smart answers

The app thinks deeply, pulls exact parts from your papers, and gives you a clear response with source links.

6
📝 Save and revisit chats

Your conversations are saved automatically, so you can switch between topics easily.

🎉 Master your papers

You now understand complex research quickly and confidently, with every fact backed by precise references.

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AI-Generated Review

What is scholar-rag?

Scholar-rag is a beginner-friendly Python project that lets you upload academic PDFs and query them via a clean React chat interface, delivering cited answers grounded in paper content. It handles full RAG pipelines—parsing sections/tables/figures, hybrid retrieval, and agentic answer generation—for precise Q&A on research papers. Built with LangGraph for orchestration, Milvus for vector search, and FastAPI backend, users get docker-compose quickstarts and API endpoints for chat/sessions/files.

Why is it gaining traction?

This beginner-friendly GitHub repo stands out with its end-to-end agentic workflow: multi-turn memory, query decomposition, self-reflection retries, and lazy VLM for figure analysis, all in a modular setup ideal for secondary development. Devs notice the built-in RAGAS evals, smart OCR fallback, and source-level citations (paper/section/page), making it a practical scholar rag alternative to fragmented tools. Its extensibility hooks beginners exploring agentic RAG without setup headaches.

Who should use this?

Students prototyping academic search tools, researchers needing quick paper Q&A with citations, or Python devs learning agentic systems via beginner-friendly open source projects on GitHub. Perfect for both learning the full pipeline and building custom scholar rag apps, like analyzing arXiv dumps or integrating with Google Scholar workflows.

Verdict

Grab this 11-star beginner-friendly Python project if you're dipping into agentic RAG—docs, Makefile, and evals make it a solid learning repo despite 1.0% credibility score. Maturity is early (low activity), so fork for production, but it's extensible enough for real secondary dev right now.

(198 words)

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