ai-research-assistant-RAG-multi-agent-system
View on GitHubRAG-based AI research assistant that lets users upload research papers, perform semantic search, and chat with documents using LLM-powered responses.
Category
AI/ML
Role
Lead AI Engineer
Timeline
4 Months
Tech Stack
ai-research-assistant-RAG-multi-agent-system
System Architecture
A high-level overview of the technical components and data flow that power ai-research-assistant-RAG-multi-agent-system.
The Approach
Architected a RAG-powered AI research assistant that streamlines academic literature analysis, enabling users to upload research papers, perform semantic search across document repositories, and engage in contextual conversations with their document library. Built a modular multi-agent pipeline using FastAPI backend that handles document parsing, chunking, and embedding generation, with pgvector for efficient similarity search over millions of research paper chunks. The Next.js frontend provides a clean, intuitive interface for document management, real-time chat interactions, and search result visualization, while implementing streaming responses for seamless user experience. Integrated OpenAI's GPT-4 to generate accurate, cited responses that reference specific sections of uploaded papers, with automatic citation tracking and source highlighting.
Key Challenges
- Building an accurate document chunking strategy that preserves context across research paper sections while maintaining vector search quality.
- Optimizing RAG retrieval latency to deliver semantic search results in under 200ms for large document libraries with 10k+ papers.
- Implementing citation tracking that accurately links LLM responses back to their original source paragraphs in source documents.
- Creating a scalable document processing pipeline that handles PDF parsing, OCR for scanned papers, and concurrent embedding generation.
- Maintaining context window limits during multi-turn conversations while still referencing relevant information from 1000+ page document sets.