Academic RAG Assistant
Research paper Q&A assistant built with FastAPI, LangChain, and ChromaDB vector store with autonomous web search capabilities.
Role Full-Stack Developer
Release date 2026-03
Category AI, Web
Technology
Python FastAPI LangChain ChromaDB React Tailwind CSS
Project Overview
Academic RAG Assistant is an information retrieval system based on Retrieval-Augmented Generation (RAG) designed to help students and researchers read, synthesize, and query scientific literature with strict factual grounding.
The system indexes PDF and DOCX papers into semantic vector collections in ChromaDB, providing an interactive conversational workspace powered by a tool-calling agent that cross-references uploaded papers and web search.
Problems
- Literature Synthesis Burden: Reviewing methodology, limitations, and findings across dozens of academic journals requires extensive time.
- LLM Hallucinations: Unconstrained language models often invent citations or claims absent from the original corpus.
- Document Complexity: Academic papers frequently feature multi-column layouts, data tables, and dense references.
Target Users
- Undergraduate and Graduate Students: Reviewing theoretical foundations and literature reviews for thesis projects.
- Faculty and Researchers: Conducting comparative reviews across published research papers.
Technical Decisions
- Local Vector Storage with ChromaDB: Fast chunk indexing and cosine similarity search without reliance on costly external vector services.
- LangChain Tool-Calling Agent: The model first searches uploaded document chunks and autonomously triggers web search only when recent corroboration is needed.
- Asynchronous FastAPI Backend: Efficiently handles large file uploads, text chunking, and streaming answer tokens to client interfaces.
- Interactive React & Tailwind Interface: Research workspace with session history and clear source citation previews.
Challenges
- Academic Text Chunking: Segmenting paper content without breaking semantic context or disconnecting formulas and tables.
- Citation Precision: Guarding the agent to prioritize direct quotes and references from uploaded documents over generalized knowledge.
Measurable Results
- Source Grounding: Generated answers consistently provide direct references to relevant sections of the source documents.
- Multi-Document Support: Capable of processing multi-chapter PDF documents and organizing them into separate research collections.